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2020


Label Efficient Visual Abstractions for Autonomous Driving
Label Efficient Visual Abstractions for Autonomous Driving

Behl, A., Chitta, K., Prakash, A., Ohn-Bar, E., Geiger, A.

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, October 2020 (conference)

Abstract
It is well known that semantic segmentation can be used as an effective intermediate representation for learning driving policies. However, the task of street scene semantic segmentation requires expensive annotations. Furthermore, segmentation algorithms are often trained irrespective of the actual driving task, using auxiliary image-space loss functions which are not guaranteed to maximize driving metrics such as safety or distance traveled per intervention. In this work, we seek to quantify the impact of reducing segmentation annotation costs on learned behavior cloning agents. We analyze several segmentation-based intermediate representations. We use these visual abstractions to systematically study the trade-off between annotation efficiency and driving performance, ie, the types of classes labeled, the number of image samples used to learn the visual abstraction model, and their granularity (eg, object masks vs. 2D bounding boxes). Our analysis uncovers several practical insights into how segmentation-based visual abstractions can be exploited in a more label efficient manner. Surprisingly, we find that state-of-the-art driving performance can be achieved with orders of magnitude reduction in annotation cost. Beyond label efficiency, we find several additional training benefits when leveraging visual abstractions, such as a significant reduction in the variance of the learned policy when compared to state-of-the-art end-to-end driving models.

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pdf slides video Project Page [BibTex]

2020


pdf slides video Project Page [BibTex]


Learning of sub-optimal gait controllers for magnetic walking soft millirobots
Learning of sub-optimal gait controllers for magnetic walking soft millirobots

Culha, U., Demir, S. O., Trimpe, S., Sitti, M.

In Proceedings of Robotics: Science and Systems, July 2020, Culha and Demir are equally contributing authors (inproceedings)

Abstract
Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can access confined spaces in the human body. However, due to highly nonlinear soft continuum deformation kinematics, inherent stochastic variability during fabrication at the small scale, and lack of accurate models, the conventional control methods cannot be easily applied. Adaptivity of robot control is additionally crucial for medical operations, as operation environments show large variability, and robot materials may degrade or change over time,which would have deteriorating effects on the robot motion and task performance. Therefore, we propose using a probabilistic learning approach for millimeter-scale magnetic walking soft robots using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme to find controller parameters while optimizing the stride length performance of the walking soft millirobot robot within a small number of physical experiments. We demonstrate adaptation to fabrication variabilities in three different robots and to walking surfaces with different roughness. We also show an improvement in the learning performance by transferring the learning results of one robot to the others as prior information.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


{GENTEL : GENerating Training data Efficiently for Learning to segment medical images}
GENTEL : GENerating Training data Efficiently for Learning to segment medical images

Thakur, R. P., Rocamora, S. P., Goel, L., Pohmann, R., Machann, J., Black, M. J.

Congrès Reconnaissance des Formes, Image, Apprentissage et Perception (RFAIP), June 2020 (conference)

Abstract
Accurately segmenting MRI images is crucial for many clinical applications. However, manually segmenting images with accurate pixel precision is a tedious and time consuming task. In this paper we present a simple, yet effective method to improve the efficiency of the image segmentation process. We propose to transform the image annotation task into a binary choice task. We start by using classical image processing algorithms with different parameter values to generate multiple, different segmentation masks for each input MRI image. Then, instead of segmenting the pixels of the images, the user only needs to decide whether a segmentation is acceptable or not. This method allows us to efficiently obtain high quality segmentations with minor human intervention. With the selected segmentations, we train a state-of-the-art neural network model. For the evaluation, we use a second MRI dataset (1.5T Dataset), acquired with a different protocol and containing annotations. We show that the trained network i) is able to automatically segment cases where none of the classical methods obtain a high quality result ; ii) generalizes to the second MRI dataset, which was acquired with a different protocol and was never seen at training time ; and iii) enables detection of miss-annotations in this second dataset. Quantitatively, the trained network obtains very good results: DICE score - mean 0.98, median 0.99- and Hausdorff distance (in pixels) - mean 4.7, median 2.0-.

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[BibTex]

[BibTex]


Learning to Dress 3D People in Generative Clothing
Learning to Dress 3D People in Generative Clothing

Ma, Q., Yang, J., Ranjan, A., Pujades, S., Pons-Moll, G., Tang, S., Black, M. J.

In Computer Vision and Pattern Recognition (CVPR), June 2020 (inproceedings)

Abstract
Three-dimensional human body models are widely used in the analysis of human pose and motion. Existing models, however, are learned from minimally-clothed 3D scans and thus do not generalize to the complexity of dressed people in common images and videos. Additionally, current models lack the expressive power needed to represent the complex non-linear geometry of pose-dependent clothing shape. To address this, we learn a generative 3D mesh model of clothed people from 3D scans with varying pose and clothing. Specifically, we train a conditional Mesh-VAE-GAN to learn the clothing deformation from the SMPL body model, making clothing an additional term on SMPL. Our model is conditioned on both pose and clothing type, giving the ability to draw samples of clothing to dress different body shapes in a variety of styles and poses. To preserve wrinkle detail, our Mesh-VAE-GAN extends patchwise discriminators to 3D meshes. Our model, named CAPE, represents global shape and fine local structure, effectively extending the SMPL body model to clothing. To our knowledge, this is the first generative model that directly dresses 3D human body meshes and generalizes to different poses.

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arxiv project page code [BibTex]


Generating 3D People in Scenes without People
Generating 3D People in Scenes without People

Zhang, Y., Hassan, M., Neumann, H., Black, M. J., Tang, S.

In Computer Vision and Pattern Recognition (CVPR), June 2020 (inproceedings)

Abstract
We present a fully automatic system that takes a 3D scene and generates plausible 3D human bodies that are posed naturally in that 3D scene. Given a 3D scene without people, humans can easily imagine how people could interact with the scene and the objects in it. However, this is a challenging task for a computer as solving it requires that (1) the generated human bodies to be semantically plausible within the 3D environment (e.g. people sitting on the sofa or cooking near the stove), and (2) the generated human-scene interaction to be physically feasible such that the human body and scene do not interpenetrate while, at the same time, body-scene contact supports physical interactions. To that end, we make use of the surface-based 3D human model SMPL-X. We first train a conditional variational autoencoder to predict semantically plausible 3D human poses conditioned on latent scene representations, then we further refine the generated 3D bodies using scene constraints to enforce feasible physical interaction. We show that our approach is able to synthesize realistic and expressive 3D human bodies that naturally interact with 3D environment. We perform extensive experiments demonstrating that our generative framework compares favorably with existing methods, both qualitatively and quantitatively. We believe that our scene-conditioned 3D human generation pipeline will be useful for numerous applications; e.g. to generate training data for human pose estimation, in video games and in VR/AR. Our project page for data and code can be seen at: \url{https://vlg.inf.ethz.ch/projects/PSI/}.

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Code PDF [BibTex]

Code PDF [BibTex]


Learning Physics-guided Face Relighting under Directional Light
Learning Physics-guided Face Relighting under Directional Light

Nestmeyer, T., Lalonde, J., Matthews, I., Lehrmann, A. M.

In Conference on Computer Vision and Pattern Recognition, IEEE/CVF, June 2020 (inproceedings) Accepted

Abstract
Relighting is an essential step in realistically transferring objects from a captured image into another environment. For example, authentic telepresence in Augmented Reality requires faces to be displayed and relit consistent with the observer's scene lighting. We investigate end-to-end deep learning architectures that both de-light and relight an image of a human face. Our model decomposes the input image into intrinsic components according to a diffuse physics-based image formation model. We enable non-diffuse effects including cast shadows and specular highlights by predicting a residual correction to the diffuse render. To train and evaluate our model, we collected a portrait database of 21 subjects with various expressions and poses. Each sample is captured in a controlled light stage setup with 32 individual light sources. Our method creates precise and believable relighting results and generalizes to complex illumination conditions and challenging poses, including when the subject is not looking straight at the camera.

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Paper [BibTex]

Paper [BibTex]


{VIBE}: Video Inference for Human Body Pose and Shape Estimation
VIBE: Video Inference for Human Body Pose and Shape Estimation

Kocabas, M., Athanasiou, N., Black, M. J.

In Computer Vision and Pattern Recognition (CVPR), June 2020 (inproceedings)

Abstract
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methodsfail to produce accurate and natural motion sequences due to a lack of ground-truth 3D motion data for training. To address this problem, we propose “Video Inference for Body Pose and Shape Estimation” (VIBE), which makes use of an existing large-scale motion capture dataset (AMASS) together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty is an adversarial learning framework that leverages AMASS to discriminate between real human motions and those produced by our temporal pose and shape regression networks. We define a temporal network architecture and show that adversarial training, at the sequence level, produces kinematically plausible motion sequences without in-the-wild ground-truth 3D labels. We perform extensive experimentation to analyze the importance of motion and demonstrate the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving state-of-the-art performance. Code and pretrained models are available at https://github.com/mkocabas/VIBE

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arXiv code video supplemental video [BibTex]


From Variational to Deterministic Autoencoders
From Variational to Deterministic Autoencoders

Ghosh*, P., Sajjadi*, M. S. M., Vergari, A., Black, M. J., Schölkopf, B.

8th International Conference on Learning Representations (ICLR) , April 2020, *equal contribution (conference) Accepted

Abstract
Variational Autoencoders (VAEs) provide a theoretically-backed framework for deep generative models. However, they often produce “blurry” images, which is linked to their training objective. Sampling in the most popular implementation, the Gaussian VAE, can be interpreted as simply injecting noise to the input of a deterministic decoder. In practice, this simply enforces a smooth latent space structure. We challenge the adoption of the full VAE framework on this specific point in favor of a simpler, deterministic one. Specifically, we investigate how substituting stochasticity with other explicit and implicit regularization schemes can lead to a meaningful latent space without having to force it to conform to an arbitrarily chosen prior. To retrieve a generative mechanism for sampling new data points, we propose to employ an efficient ex-post density estimation step that can be readily adopted both for the proposed deterministic autoencoders as well as to improve sample quality of existing VAEs. We show in a rigorous empirical study that regularized deterministic autoencoding achieves state-of-the-art sample quality on the common MNIST, CIFAR-10 and CelebA datasets.

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arXiv [BibTex]

arXiv [BibTex]


Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations
Chained Representation Cycling: Learning to Estimate 3D Human Pose and Shape by Cycling Between Representations

Rueegg, N., Lassner, C., Black, M. J., Schindler, K.

In Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), Febuary 2020 (inproceedings)

Abstract
The goal of many computer vision systems is to transform image pixels into 3D representations. Recent popular models use neural networks to regress directly from pixels to 3D object parameters. Such an approach works well when supervision is available, but in problems like human pose and shape estimation, it is difficult to obtain natural images with 3D ground truth. To go one step further, we propose a new architecture that facilitates unsupervised, or lightly supervised, learning. The idea is to break the problem into a series of transformations between increasingly abstract representations. Each step involves a cycle designed to be learnable without annotated training data, and the chain of cycles delivers the final solution. Specifically, we use 2D body part segments as an intermediate representation that contains enough information to be lifted to 3D, and at the same time is simple enough to be learned in an unsupervised way. We demonstrate the method by learning 3D human pose and shape from un-paired and un-annotated images. We also explore varying amounts of paired data and show that cycling greatly alleviates the need for paired data. While we present results for modeling humans, our formulation is general and can be applied to other vision problems.

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pdf [BibTex]

pdf [BibTex]


Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image
Learning Unsupervised Hierarchical Part Decomposition of 3D Objects from a Single RGB Image

Paschalidou, D., Gool, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Humans perceive the 3D world as a set of distinct objects that are characterized by various low-level (geometry, reflectance) and high-level (connectivity, adjacency, symmetry) properties. Recent methods based on convolutional neural networks (CNNs) demonstrated impressive progress in 3D reconstruction, even when using a single 2D image as input. However, the majority of these methods focuses on recovering the local 3D geometry of an object without considering its part-based decomposition or relations between parts. We address this challenging problem by proposing a novel formulation that allows to jointly recover the geometry of a 3D object as a set of primitives as well as their latent hierarchical structure without part-level supervision. Our model recovers the higher level structural decomposition of various objects in the form of a binary tree of primitives, where simple parts are represented with fewer primitives and more complex parts are modeled with more components. Our experiments on the ShapeNet and D-FAUST datasets demonstrate that considering the organization of parts indeed facilitates reasoning about 3D geometry.

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pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]

pdf suppmat Video 2 Project Page Slides Poster Video 1 [BibTex]


Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils
Towards 5-DoF Control of an Untethered Magnetic Millirobot via MRI Gradient Coils

Onder Erin, D. A. M. E. T., Sitti, M.

In IEEE International Conference on Robotics and Automation (ICRA), 2020 (inproceedings)

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[BibTex]

[BibTex]


Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

Liao, Y., Schwarz, K., Mescheder, L., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.

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pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Slides Poster [BibTex]


Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving
Exploring Data Aggregation in Policy Learning for Vision-based Urban Autonomous Driving

Prakash, A., Behl, A., Ohn-Bar, E., Chitta, K., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Data aggregation techniques can significantly improve vision-based policy learning within a training environment, e.g., learning to drive in a specific simulation condition. However, as on-policy data is sequentially sampled and added in an iterative manner, the policy can specialize and overfit to the training conditions. For real-world applications, it is useful for the learned policy to generalize to novel scenarios that differ from the training conditions. To improve policy learning while maintaining robustness when training end-to-end driving policies, we perform an extensive analysis of data aggregation techniques in the CARLA environment. We demonstrate how the majority of them have poor generalization performance, and develop a novel approach with empirically better generalization performance compared to existing techniques. Our two key ideas are (1) to sample critical states from the collected on-policy data based on the utility they provide to the learned policy in terms of driving behavior, and (2) to incorporate a replay buffer which progressively focuses on the high uncertainty regions of the policy's state distribution. We evaluate the proposed approach on the CARLA NoCrash benchmark, focusing on the most challenging driving scenarios with dense pedestrian and vehicle traffic. Our approach improves driving success rate by 16% over state-of-the-art, achieving 87% of the expert performance while also reducing the collision rate by an order of magnitude without the use of any additional modality, auxiliary tasks, architectural modifications or reward from the environment.

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pdf suppmat Video 2 Project Page Slides Video 1 [BibTex]

pdf suppmat Video 2 Project Page Slides Video 1 [BibTex]


Learning Situational Driving
Learning Situational Driving

Ohn-Bar, E., Prakash, A., Behl, A., Chitta, K., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Human drivers have a remarkable ability to drive in diverse visual conditions and situations, e.g., from maneuvering in rainy, limited visibility conditions with no lane markings to turning in a busy intersection while yielding to pedestrians. In contrast, we find that state-of-the-art sensorimotor driving models struggle when encountering diverse settings with varying relationships between observation and action. To generalize when making decisions across diverse conditions, humans leverage multiple types of situation-specific reasoning and learning strategies. Motivated by this observation, we develop a framework for learning a situational driving policy that effectively captures reasoning under varying types of scenarios. Our key idea is to learn a mixture model with a set of policies that can capture multiple driving modes. We first optimize the mixture model through behavior cloning, and show it to result in significant gains in terms of driving performance in diverse conditions. We then refine the model by directly optimizing for the driving task itself, i.e., supervised with the navigation task reward. Our method is more scalable than methods assuming access to privileged information, e.g., perception labels, as it only assumes demonstration and reward-based supervision. We achieve over 98% success rate on the CARLA driving benchmark as well as state-of-the-art performance on a newly introduced generalization benchmark.

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pdf suppmat Video 2 Project Page Video 1 Slides [BibTex]

pdf suppmat Video 2 Project Page Video 1 Slides [BibTex]


On Joint Estimation of Pose, Geometry and svBRDF from a Handheld Scanner
On Joint Estimation of Pose, Geometry and svBRDF from a Handheld Scanner

Schmitt, C., Donne, S., Riegler, G., Koltun, V., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
We propose a novel formulation for joint recovery of camera pose, object geometry and spatially-varying BRDF. The input to our approach is a sequence of RGB-D images captured by a mobile, hand-held scanner that actively illuminates the scene with point light sources. Compared to previous works that jointly estimate geometry and materials from a hand-held scanner, we formulate this problem using a single objective function that can be minimized using off-the-shelf gradient-based solvers. By integrating material clustering as a differentiable operation into the optimization process, we avoid pre-processing heuristics and demonstrate that our model is able to determine the correct number of specular materials independently. We provide a study on the importance of each component in our formulation and on the requirements of the initial geometry. We show that optimizing over the poses is crucial for accurately recovering fine details and that our approach naturally results in a semantically meaningful material segmentation.

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pdf Project Page Slides Video Poster [BibTex]

pdf Project Page Slides Video Poster [BibTex]


Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision
Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision

Niemeyer, M., Mescheder, L., Oechsle, M., Geiger, A.

In Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2020, 2020 (inproceedings)

Abstract
Learning-based 3D reconstruction methods have shown impressive results. However, most methods require 3D supervision which is often hard to obtain for real-world datasets. Recently, several works have proposed differentiable rendering techniques to train reconstruction models from RGB images. Unfortunately, these approaches are currently restricted to voxel- and mesh-based representations, suffering from discretization or low resolution. In this work, we propose a differentiable rendering formulation for implicit shape and texture representations. Implicit representations have recently gained popularity as they represent shape and texture continuously. Our key insight is that depth gradients can be derived analytically using the concept of implicit differentiation. This allows us to learn implicit shape and texture representations directly from RGB images. We experimentally show that our single-view reconstructions rival those learned with full 3D supervision. Moreover, we find that our method can be used for multi-view 3D reconstruction, directly resulting in watertight meshes.

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pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]

pdf suppmat Video 2 Project Page Video 1 Video 3 Slides Poster [BibTex]


Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art
Computer Vision for Autonomous Vehicles: Problems, Datasets and State-of-the-Art

Janai, J., Güney, F., Behl, A., Geiger, A.

Arxiv, Foundations and Trends in Computer Graphics and Vision, 2020 (book)

Abstract
Recent years have witnessed enormous progress in AI-related fields such as computer vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it becomes increasingly difficult to stay up-to-date or enter the field as a beginner. While several survey papers on particular sub-problems have appeared, no comprehensive survey on problems, datasets, and methods in computer vision for autonomous vehicles has been published. This monograph attempts to narrow this gap by providing a survey on the state-of-the-art datasets and techniques. Our survey includes both the historically most relevant literature as well as the current state of the art on several specific topics, including recognition, reconstruction, motion estimation, tracking, scene understanding, and end-to-end learning for autonomous driving. Towards this goal, we analyze the performance of the state of the art on several challenging benchmarking datasets, including KITTI, MOT, and Cityscapes. Besides, we discuss open problems and current research challenges. To ease accessibility and accommodate missing references, we also provide a website that allows navigating topics as well as methods and provides additional information.

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pdf Project Page link Project Page [BibTex]

2017


The Numerics of GANs
The Numerics of GANs

Mescheder, L., Nowozin, S., Geiger, A.

In Proceedings from the conference "Neural Information Processing Systems 2017., (Editors: Guyon I. and Luxburg U.v. and Bengio S. and Wallach H. and Fergus R. and Vishwanathan S. and Garnett R.), Curran Associates, Inc., Advances in Neural Information Processing Systems 30 (NIPS), December 2017 (inproceedings)

Abstract
In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.

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pdf Project Page [BibTex]

2017


pdf Project Page [BibTex]


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Synchronicity Trumps Mischief in Rhythmic Human-Robot Social-Physical Interaction

Fitter, N. T., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robotics Research (ISRR), Puerto Varas, Chile, December 2017 (inproceedings) In press

Abstract
Hand-clapping games and other forms of rhythmic social-physical interaction might help foster human-robot teamwork, but the design of such interactions has scarcely been explored. We leveraged our prior work to enable the Rethink Robotics Baxter Research Robot to competently play one-handed tempo-matching hand-clapping games with a human user. To understand how such a robot’s capabilities and behaviors affect user perception, we created four versions of this interaction: the hand clapping could be initiated by either the robot or the human, and the non-initiating partner could be either cooperative, yielding synchronous motion, or mischievously uncooperative. Twenty adults tested two clapping tempos in each of these four interaction modes in a random order, rating every trial on standardized scales. The study results showed that having the robot initiate the interaction gave it a more dominant perceived personality. Despite previous results on the intrigue of misbehaving robots, we found that moving synchronously with the robot almost always made the interaction more enjoyable, less mentally taxing, less physically demanding, and lower effort for users than asynchronous interactions caused by robot or human mischief. Taken together, our results indicate that cooperative rhythmic social-physical interaction has the potential to strengthen human-robot partnerships.

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[BibTex]

[BibTex]


A Generative Model of People in Clothing
A Generative Model of People in Clothing

Lassner, C., Pons-Moll, G., Gehler, P. V.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract
We present the first image-based generative model of people in clothing in a full-body setting. We sidestep the commonly used complex graphics rendering pipeline and the need for high-quality 3D scans of dressed people. Instead, we learn generative models from a large image database. The main challenge is to cope with the high variance in human pose, shape and appearance. For this reason, pure image-based approaches have not been considered so far. We show that this challenge can be overcome by splitting the generating process in two parts. First, we learn to generate a semantic segmentation of the body and clothing. Second, we learn a conditional model on the resulting segments that creates realistic images. The full model is differentiable and can be conditioned on pose, shape or color. The result are samples of people in different clothing items and styles. The proposed model can generate entirely new people with realistic clothing. In several experiments we present encouraging results that suggest an entirely data-driven approach to people generation is possible.

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link (url) Project Page [BibTex]

link (url) Project Page [BibTex]


Semantic Video CNNs through Representation Warping
Semantic Video CNNs through Representation Warping

Gadde, R., Jampani, V., Gehler, P. V.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings) Accepted

Abstract
In this work, we propose a technique to convert CNN models for semantic segmentation of static images into CNNs for video data. We describe a warping method that can be used to augment existing architectures with very lit- tle extra computational cost. This module is called Net- Warp and we demonstrate its use for a range of network architectures. The main design principle is to use optical flow of adjacent frames for warping internal network repre- sentations across time. A key insight of this work is that fast optical flow methods can be combined with many different CNN architectures for improved performance and end-to- end training. Experiments validate that the proposed ap- proach incurs only little extra computational cost, while im- proving performance, when video streams are available. We achieve new state-of-the-art results on the standard CamVid and Cityscapes benchmark datasets and show reliable im- provements over different baseline networks. Our code and models are available at http://segmentation.is. tue.mpg.de

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pdf Supplementary Project Page [BibTex]

pdf Supplementary Project Page [BibTex]


Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?
Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?

Behl, A., Jafari, O. H., Mustikovela, S. K., Alhaija, H. A., Rother, C., Geiger, A.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract
Existing methods for 3D scene flow estimation often fail in the presence of large displacement or local ambiguities, e.g., at texture-less or reflective surfaces. However, these challenges are omnipresent in dynamic road scenes, which is the focus of this work. Our main contribution is to overcome these 3D motion estimation problems by exploiting recognition. In particular, we investigate the importance of recognition granularity, from coarse 2D bounding box estimates over 2D instance segmentations to fine-grained 3D object part predictions. We compute these cues using CNNs trained on a newly annotated dataset of stereo images and integrate them into a CRF-based model for robust 3D scene flow estimation - an approach we term Instance Scene Flow. We analyze the importance of each recognition cue in an ablation study and observe that the instance segmentation cue is by far strongest, in our setting. We demonstrate the effectiveness of our method on the challenging KITTI 2015 scene flow benchmark where we achieve state-of-the-art performance at the time of submission.

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pdf suppmat Poster Project Page [BibTex]

pdf suppmat Poster Project Page [BibTex]


A simple yet effective baseline for 3d human pose estimation
A simple yet effective baseline for 3d human pose estimation

Martinez, J., Hossain, R., Romero, J., Little, J. J.

In Proceedings IEEE International Conference on Computer Vision (ICCV), IEEE, Piscataway, NJ, USA, IEEE International Conference on Computer Vision (ICCV), October 2017 (inproceedings)

Abstract
Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3-dimensional positions. With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feed-forward network outperforms the best reported result by about 30\% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (\ie, using images as input) yields state of the art results -- this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.

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video code arxiv pdf preprint Project Page [BibTex]

video code arxiv pdf preprint Project Page [BibTex]


Sparsity Invariant CNNs
Sparsity Invariant CNNs

Uhrig, J., Schneider, N., Schneider, L., Franke, U., Brox, T., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments \wrt various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


OctNetFusion: Learning Depth Fusion from Data
OctNetFusion: Learning Depth Fusion from Data

Riegler, G., Ulusoy, A. O., Bischof, H., Geiger, A.

International Conference on 3D Vision (3DV) 2017, International Conference on 3D Vision (3DV), October 2017 (conference)

Abstract
In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on averaging truncated signed distance functions, which was originally proposed by Curless and Levoy in 1996. While this method is simple and provides great results, it is not able to reconstruct (partially) occluded surfaces and requires a large number frames to filter out sensor noise and outliers. Motivated by the availability of large 3D model repositories and recent advances in deep learning, we present a novel 3D CNN architecture that learns to predict an implicit surface representation from the input depth maps. Our learning based method significantly outperforms the traditional volumetric fusion approach in terms of noise reduction and outlier suppression. By learning the structure of real world 3D objects and scenes, our approach is further able to reconstruct occluded regions and to fill in gaps in the reconstruction. We demonstrate that our learning based approach outperforms both vanilla TSDF fusion as well as TV-L1 fusion on the task of volumetric fusion. Further, we demonstrate state-of-the-art 3D shape completion results.

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pdf Video 1 Video 2 Project Page Project Page [BibTex]

pdf Video 1 Video 2 Project Page Project Page [BibTex]


Direct Visual Odometry for a Fisheye-Stereo Camera
Direct Visual Odometry for a Fisheye-Stereo Camera

Liu, P., Heng, L., Sattler, T., Geiger, A., Pollefeys, M.

In Proceedings IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, Piscataway, NJ, USA, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2017 (inproceedings)

Abstract
We present a direct visual odometry algorithm for a fisheye-stereo camera. Our algorithm performs simultaneous camera motion estimation and semi-dense reconstruction. The pipeline consists of two threads: a tracking thread and a mapping thread. In the tracking thread, we estimate the camera pose via semi-dense direct image alignment. To have a wider field of view (FoV) which is important for robotic perception, we use fisheye images directly without converting them to conventional pinhole images which come with a limited FoV. To address the epipolar curve problem, plane-sweeping stereo is used for stereo matching and depth initialization. Multiple depth hypotheses are tracked for selected pixels to better capture the uncertainty characteristics of stereo matching. Temporal motion stereo is then used to refine the depth and remove false positive depth hypotheses. Our implementation runs at an average of 20 Hz on a low-end PC. We run experiments in outdoor environments to validate our algorithm, and discuss the experimental results. We experimentally show that we are able to estimate 6D poses with low drift, and at the same time, do semi-dense 3D reconstruction with high accuracy.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


A Robotic Framework to Overcome Sensory Overload in Children on the Autism Spectrum: A Pilot Study
A Robotic Framework to Overcome Sensory Overload in Children on the Autism Spectrum: A Pilot Study

Javed, H., Burns, R., Jeon, M., Howard, A., Park, C. H.

In International Conference on Intelligent Robots and Systems (IROS) 2017, International Conference on Intelligent Robots and Systems, September 2017 (inproceedings)

Abstract
This paper discusses a novel framework designed to provide sensory stimulation to children with Autism Spectrum Disorder (ASD). The set up consists of multi-sensory stations to stimulate visual/auditory/olfactory/gustatory/tactile/vestibular senses, together with a robotic agent that navigates through each station responding to the different stimuli. We hypothesize that the robot’s responses will help children learn acceptable ways to respond to stimuli that might otherwise trigger sensory overload. Preliminary results from a pilot study conducted to examine the effectiveness of such a setup were encouraging and are described briefly in this text.

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[BibTex]

[BibTex]


An Interactive Robotic System for Promoting Social Engagement
An Interactive Robotic System for Promoting Social Engagement

Burns, R., Javed, H., Jeon, M., Howard, A., Park, C. H.

In International Conference on Intelligent Robots and Systems (IROS) 2017, International Conference on Intelligent Robots and Systems, September 2017 (inproceedings)

Abstract
This abstract (and poster) is a condensed version of Burns' Master's thesis and related journal article. It discusses the use of imitation via robotic motion learning to improve human-robot interaction. It focuses on the preliminary results from a pilot study of 12 subjects. We hypothesized that the robot's use of imitation will increase the user's openness towards engaging with the robot. Post-imitation, experimental subjects displayed a more positive emotional state, had higher instances of mood contagion towards the robot, and interpreted the robot to have a higher level of autonomy than their control group counterparts. These results point to an increased user interest in engagement fueled by personalized imitation during interaction.

hi

[BibTex]

[BibTex]


Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes
Augmented Reality Meets Deep Learning for Car Instance Segmentation in Urban Scenes

Alhaija, H. A., Mustikovela, S. K., Mescheder, L., Geiger, A., Rother, C.

In Proceedings of the British Machine Vision Conference 2017, Proceedings of the British Machine Vision Conference, September 2017 (inproceedings)

Abstract
The success of deep learning in computer vision is based on the availability of large annotated datasets. To lower the need for hand labeled images, virtually rendered 3D worlds have recently gained popularity. Unfortunately, creating realistic 3D content is challenging on its own and requires significant human effort. In this work, we propose an alternative paradigm which combines real and synthetic data for learning semantic instance segmentation models. Exploiting the fact that not all aspects of the scene are equally important for this task, we propose to augment real-world imagery with virtual objects of the target category. Capturing real-world images at large scale is easy and cheap, and directly provides real background appearances without the need for creating complex 3D models of the environment. We present an efficient procedure to augment these images with virtual objects. This allows us to create realistic composite images which exhibit both realistic background appearance as well as a large number of complex object arrangements. In contrast to modeling complete 3D environments, our data augmentation approach requires only a few user interactions in combination with 3D shapes of the target object category. We demonstrate the utility of the proposed approach for training a state-of-the-art high-capacity deep model for semantic instance segmentation. In particular, we consider the task of segmenting car instances on the KITTI dataset which we have annotated with pixel-accurate ground truth. Our experiments demonstrate that models trained on augmented imagery generalize better than those trained on synthetic data or models trained on limited amounts of annotated real data.

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pdf Project Page [BibTex]

pdf Project Page [BibTex]


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Swimming in low reynolds numbers using planar and helical flagellar waves

Khalil, I. S. M., Tabak, A. F., Seif, M. A., Klingner, A., Adel, B., Sitti, M.

In International Conference on Intelligent Robots and Systems (IROS) 2017, pages: 1907-1912, International Conference on Intelligent Robots and Systems, September 2017 (inproceedings)

Abstract
In travelling towards the oviducts, sperm cells undergo transitions between planar to helical flagellar propulsion by a beating tail based on the viscosity of the environment. In this work, we aim to model and mimic this behaviour in low Reynolds number fluids using externally actuated soft robotic sperms. We numerically investigate the effects of transition between planar to helical flagellar propulsion on the swimming characteristics of the robotic sperm using a model based on resistive-force theory to study the role of viscous forces on its flexible tail. Experimental results are obtained using robots that contain magnetic particles within the polymer matrix of its head and an ultra-thin flexible tail. The planar and helical flagellar propulsion are achieved using in-plane and out-of-plane uniform fields with sinusoidally varying components, respectively. We experimentally show that the swimming speed of the robotic sperm increases by a factor of 1.4 (fluid viscosity 5 Pa.s) when it undergoes a controlled transition between planar to helical flagellar propulsion, at relatively low actuation frequencies.

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DOI [BibTex]

DOI [BibTex]


 Effects of animation retargeting on perceived action outcomes
Effects of animation retargeting on perceived action outcomes

Kenny, S., Mahmood, N., Honda, C., Black, M. J., Troje, N. F.

Proceedings of the ACM Symposium on Applied Perception (SAP’17), pages: 2:1-2:7, September 2017 (conference)

Abstract
The individual shape of the human body, including the geometry of its articulated structure and the distribution of weight over that structure, influences the kinematics of a person's movements. How sensitive is the visual system to inconsistencies between shape and motion introduced by retargeting motion from one person onto the shape of another? We used optical motion capture to record five pairs of male performers with large differences in body weight, while they pushed, lifted, and threw objects. Based on a set of 67 markers, we estimated both the kinematics of the actions as well as the performer's individual body shape. To obtain consistent and inconsistent stimuli, we created animated avatars by combining the shape and motion estimates from either a single performer or from different performers. In a virtual reality environment, observers rated the perceived weight or thrown distance of the objects. They were also asked to explicitly discriminate between consistent and hybrid stimuli. Observers were unable to accomplish the latter, but hybridization of shape and motion influenced their judgements of action outcome in systematic ways. Inconsistencies between shape and motion were assimilated into an altered perception of the action outcome.

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pdf DOI [BibTex]

pdf DOI [BibTex]


Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks
Adversarial Variational Bayes: Unifying Variational Autoencoders and Generative Adversarial Networks

Mescheder, L., Nowozin, S., Geiger, A.

In Proceedings of the 34th International Conference on Machine Learning, 70, Proceedings of Machine Learning Research, (Editors: Doina Precup, Yee Whye Teh), PMLR, International Conference on Machine Learning (ICML), August 2017 (inproceedings)

Abstract
Variational Autoencoders (VAEs) are expressive latent variable models that can be used to learn complex probability distributions from training data. However, the quality of the resulting model crucially relies on the expressiveness of the inference model. We introduce Adversarial Variational Bayes (AVB), a technique for training Variational Autoencoders with arbitrarily expressive inference models. We achieve this by introducing an auxiliary discriminative network that allows to rephrase the maximum-likelihood-problem as a two-player game, hence establishing a principled connection between VAEs and Generative Adversarial Networks (GANs). We show that in the nonparametric limit our method yields an exact maximum-likelihood assignment for the parameters of the generative model, as well as the exact posterior distribution over the latent variables given an observation. Contrary to competing approaches which combine VAEs with GANs, our approach has a clear theoretical justification, retains most advantages of standard Variational Autoencoders and is easy to implement.

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pdf suppmat Project Page arxiv-version Project Page [BibTex]

pdf suppmat Project Page arxiv-version Project Page [BibTex]


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Stiffness Perception during Pinching and Dissection with Teleoperated Haptic Forceps

Ng, C., Zareinia, K., Sun, Q., Kuchenbecker, K. J.

In Proceedings of the International Symposium on Robot and Human Interactive Communication (RO-MAN), pages: 456-463, Lisbon, Portugal, August 2017 (inproceedings)

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Coupling Adaptive Batch Sizes with Learning Rates
Coupling Adaptive Batch Sizes with Learning Rates

Balles, L., Romero, J., Hennig, P.

In Proceedings Conference on Uncertainty in Artificial Intelligence (UAI) 2017, pages: 410-419, (Editors: Gal Elidan and Kristian Kersting), Association for Uncertainty in Artificial Intelligence (AUAI), Conference on Uncertainty in Artificial Intelligence (UAI), August 2017 (inproceedings)

Abstract
Mini-batch stochastic gradient descent and variants thereof have become standard for large-scale empirical risk minimization like the training of neural networks. These methods are usually used with a constant batch size chosen by simple empirical inspection. The batch size significantly influences the behavior of the stochastic optimization algorithm, though, since it determines the variance of the gradient estimates. This variance also changes over the optimization process; when using a constant batch size, stability and convergence is thus often enforced by means of a (manually tuned) decreasing learning rate schedule. We propose a practical method for dynamic batch size adaptation. It estimates the variance of the stochastic gradients and adapts the batch size to decrease the variance proportionally to the value of the objective function, removing the need for the aforementioned learning rate decrease. In contrast to recent related work, our algorithm couples the batch size to the learning rate, directly reflecting the known relationship between the two. On three image classification benchmarks, our batch size adaptation yields faster optimization convergence, while simultaneously simplifying learning rate tuning. A TensorFlow implementation is available.

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Code link (url) Project Page [BibTex]

Code link (url) Project Page [BibTex]


Learning local feature aggregation functions with backpropagation
Learning local feature aggregation functions with backpropagation

Paschalidou, D., Katharopoulos, A., Diou, C., Delopoulos, A.

In IEEE, Signal Processing Conference (EUSIPCO), 25th European, August 2017 (inproceedings)

Abstract
This paper introduces a family of local feature aggregation functions and a novel method to estimate their parameters, such that they generate optimal representations for classification (or any task that can be expressed as a cost function minimization problem). To achieve that, we compose the local feature aggregation function with the classifier cost function and we backpropagate the gradient of this cost function in order to update the local feature aggregation function parameters. Experiments on synthetic datasets indicate that our method discovers parameters that model the class-relevant information in addition to the local feature space. Further experiments on a variety of motion and visual descriptors, both on image and video datasets, show that our method outperforms other state-of-the-art local feature aggregation functions, such as Bag of Words, Fisher Vectors and VLAD, by a large margin.

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pdf code poster link (url) DOI [BibTex]

pdf code poster link (url) DOI [BibTex]


An XY ϴz flexure mechanism with optimal stiffness properties
An XY ϴz flexure mechanism with optimal stiffness properties

Lum, G. Z., Pham, M. T., Teo, T. J., Yang, G., Yeo, S. H., Sitti, M.

In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pages: 1103-1110, July 2017 (inproceedings)

Abstract
The development of optimal XY θz flexure mechanisms, which can deliver high precision motion about the z-axis, and along the x- and y-axes is highly desirable for a wide range of micro/nano-positioning tasks pertaining to biomedical research, microscopy technologies and various industrial applications. Although maximizing the stiffness ratios is a very critical design requirement, the achievable translational and rotational stiffness ratios of existing XY θz flexure mechanisms are still restricted between 0.5 and 130. As a result, these XY θz flexure mechanisms are unable to fully optimize their workspace and capabilities to reject disturbances. Here, we present an optimal XY θz flexure mechanism, which is designed to have maximum stiffness ratios. Based on finite element analysis (FEA), it has translational stiffness ratio of 248, rotational stiffness ratio of 238 and a large workspace of 2.50 mm × 2.50 mm × 10°. Despite having such a large workspace, FEA also predicts that the proposed mechanism can still achieve a high bandwidth of 70 Hz. In comparison, the bandwidth of similar existing flexure mechanisms that can deflect more than 0.5 mm or 0.5° is typically less than 45 Hz. Hence, the high stiffness ratios of the proposed mechanism are achieved without compromising its dynamic performance. Preliminary experimental results pertaining to the mechanism's translational actuating stiffness and bandwidth were in agreement with the FEA predictions as the deviation was within 10%. In conclusion, the proposed flexure mechanism exhibits superior performance and can be used across a wide range of applications.

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DOI [BibTex]

DOI [BibTex]


Positioning of drug carriers using permanent magnet-based robotic system in three-dimensional space
Positioning of drug carriers using permanent magnet-based robotic system in three-dimensional space

Khalil, I. S. M., Alfar, A., Tabak, A. F., Klingner, A., Stramigioli, S., Sitti, M.

In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM), pages: 1117-1122, July 2017 (inproceedings)

Abstract
Magnetic control of drug carriers using systems with open-configurations is essential to enable scaling to the size of in vivo applications. In this study, we demonstrate motion control of paramagnetic microparticles in a low Reynolds number fluid, using a permanent magnet-based robotic system with an open-configuration. The microparticles are controlled in three-dimensional (3D) space using a cylindrical NdFeB magnet that is fixed to the end-effector of a robotic arm. We develop a kinematic map between the position of the microparticles and the configuration of the robotic arm, and use this map as a basis of a closed-loop control system based on the position of the microparticles. Our experimental results show the ability of the robot configuration to control the exerted field gradient on the dipole of the microparticles, and achieve positioning in 3D space with maximum error of 300 µm and 600 µm in the steady-state during setpoint and trajectory tracking, respectively.

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DOI [BibTex]

DOI [BibTex]


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Self-assembly of micro/nanosystems across scales and interfaces

Mastrangeli, M.

In 2017 19th International Conference on Solid-State Sensors, Actuators and Microsystems (TRANSDUCERS), pages: 676 - 681, IEEE, July 2017 (inproceedings)

Abstract
Steady progress in understanding and implementation are establishing self-assembly as a versatile, parallel and scalable approach to the fabrication of transducers. In this contribution, I illustrate the principles and reach of self-assembly with three applications at different scales - namely, the capillary self-alignment of millimetric components, the sealing of liquid-filled polymeric microcapsules, and the accurate capillary assembly of single nanoparticles - and propose foreseeable directions for further developments.

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link (url) DOI [BibTex]

link (url) DOI [BibTex]


Joint Graph Decomposition and Node Labeling by Local Search
Joint Graph Decomposition and Node Labeling by Local Search

Levinkov, E., Uhrig, J., Tang, S., Omran, M., Insafutdinov, E., Kirillov, A., Rother, C., Brox, T., Schiele, B., Andres, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1904-1912, IEEE, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

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PDF Supplementary DOI Project Page [BibTex]

PDF Supplementary DOI Project Page [BibTex]


Dynamic {FAUST}: Registering Human Bodies in Motion
Dynamic FAUST: Registering Human Bodies in Motion

Bogo, F., Romero, J., Pons-Moll, G., Black, M. J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
While the ready availability of 3D scan data has influenced research throughout computer vision, less attention has focused on 4D data; that is 3D scans of moving nonrigid objects, captured over time. To be useful for vision research, such 4D scans need to be registered, or aligned, to a common topology. Consequently, extending mesh registration methods to 4D is important. Unfortunately, no ground-truth datasets are available for quantitative evaluation and comparison of 4D registration methods. To address this we create a novel dataset of high-resolution 4D scans of human subjects in motion, captured at 60 fps. We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology. The approach exploits consistency in texture over both short and long time intervals and deals with temporal offsets between shape and texture capture. We show how using geometry alone results in significant errors in alignment when the motions are fast and non-rigid. We evaluate the accuracy of our registration and provide a dataset of 40,000 raw and aligned meshes. Dynamic FAUST extends the popular FAUST dataset to dynamic 4D data, and is available for research purposes at http://dfaust.is.tue.mpg.de.

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pdf video Project Page Project Page Project Page [BibTex]

pdf video Project Page Project Page Project Page [BibTex]


Learning from Synthetic Humans
Learning from Synthetic Humans

Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., Schmid, C.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Estimating human pose, shape, and motion from images and videos are fundamental challenges with many applications. Recent advances in 2D human pose estimation use large amounts of manually-labeled training data for learning convolutional neural networks (CNNs). Such data is time consuming to acquire and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL tasks): a new large-scale dataset with synthetically-generated but realistic images of people rendered from 3D sequences of human motion capture data. We generate more than 6 million frames together with ground truth pose, depth maps, and segmentation masks. We show that CNNs trained on our synthetic dataset allow for accurate human depth estimation and human part segmentation in real RGB images. Our results and the new dataset open up new possibilities for advancing person analysis using cheap and large-scale synthetic data.

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arXiv project data Project Page Project Page [BibTex]

arXiv project data Project Page Project Page [BibTex]


On human motion prediction using recurrent neural networks
On human motion prediction using recurrent neural networks

Martinez, J., Black, M. J., Romero, J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Human motion modelling is a classical problem at the intersection of graphics and computer vision, with applications spanning human-computer interaction, motion synthesis, and motion prediction for virtual and augmented reality. Following the success of deep learning methods in several computer vision tasks, recent work has focused on using deep recurrent neural networks (RNNs) to model human motion, with the goal of learning time-dependent representations that perform tasks such as short-term motion prediction and long-term human motion synthesis. We examine recent work, with a focus on the evaluation methodologies commonly used in the literature, and show that, surprisingly, state-of-the-art performance can be achieved by a simple baseline that does not attempt to model motion at all. We investigate this result, and analyze recent RNN methods by looking at the architectures, loss functions, and training procedures used in state-of-the-art approaches. We propose three changes to the standard RNN models typically used for human motion, which result in a simple and scalable RNN architecture that obtains state-of-the-art performance on human motion prediction.

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arXiv Project Page [BibTex]

arXiv Project Page [BibTex]


Articulated Multi-person Tracking in the Wild
Articulated Multi-person Tracking in the Wild

Insafutdinov, E., Andriluka, M., Pishchulin, L., Tang, S., Levinkov, E., Andres, B., Schiele, B.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1293-1301, IEEE, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, Oral (inproceedings)

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DOI [BibTex]

DOI [BibTex]


Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data
Slow Flow: Exploiting High-Speed Cameras for Accurate and Diverse Optical Flow Reference Data

Janai, J., Güney, F., Wulff, J., Black, M., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 1406-1416, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Existing optical flow datasets are limited in size and variability due to the difficulty of capturing dense ground truth. In this paper, we tackle this problem by tracking pixels through densely sampled space-time volumes recorded with a high-speed video camera. Our model exploits the linearity of small motions and reasons about occlusions from multiple frames. Using our technique, we are able to establish accurate reference flow fields outside the laboratory in natural environments. Besides, we show how our predictions can be used to augment the input images with realistic motion blur. We demonstrate the quality of the produced flow fields on synthetic and real-world datasets. Finally, we collect a novel challenging optical flow dataset by applying our technique on data from a high-speed camera and analyze the performance of the state-of-the-art in optical flow under various levels of motion blur.

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pdf suppmat Project page Video DOI Project Page [BibTex]

pdf suppmat Project page Video DOI Project Page [BibTex]


Optical Flow in Mostly Rigid Scenes
Optical Flow in Mostly Rigid Scenes

Wulff, J., Sevilla-Lara, L., Black, M. J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 6911-6920, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
The optical flow of natural scenes is a combination of the motion of the observer and the independent motion of objects. Existing algorithms typically focus on either recovering motion and structure under the assumption of a purely static world or optical flow for general unconstrained scenes. We combine these approaches in an optical flow algorithm that estimates an explicit segmentation of moving objects from appearance and physical constraints. In static regions we take advantage of strong constraints to jointly estimate the camera motion and the 3D structure of the scene over multiple frames. This allows us to also regularize the structure instead of the motion. Our formulation uses a Plane+Parallax framework, which works even under small baselines, and reduces the motion estimation to a one-dimensional search problem, resulting in more accurate estimation. In moving regions the flow is treated as unconstrained, and computed with an existing optical flow method. The resulting Mostly-Rigid Flow (MR-Flow) method achieves state-of-the-art results on both the MPISintel and KITTI-2015 benchmarks.

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pdf SupMat video code Project Page [BibTex]

pdf SupMat video code Project Page [BibTex]


OctNet: Learning Deep 3D Representations at High Resolutions
OctNet: Learning Deep 3D Representations at High Resolutions

Riegler, G., Ulusoy, O., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep and high resolution. Towards this goal, we exploit the sparsity in the input data to hierarchically partition the space using a set of unbalanced octrees where each leaf node stores a pooled feature representation. This allows to focus memory allocation and computation to the relevant dense regions and enables deeper networks without compromising resolution. We demonstrate the utility of our OctNet representation by analyzing the impact of resolution on several 3D tasks including 3D object classification, orientation estimation and point cloud labeling.

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pdf suppmat Project Page Video Project Page [BibTex]

pdf suppmat Project Page Video Project Page [BibTex]


Reflectance Adaptive Filtering Improves Intrinsic Image Estimation
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation

Nestmeyer, T., Gehler, P. V.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages: 1771-1780, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

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pre-print DOI Project Page Project Page [BibTex]

pre-print DOI Project Page Project Page [BibTex]


A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos
A Multi-View Stereo Benchmark with High-Resolution Images and Multi-Camera Videos

Schöps, T., Schönberger, J. L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., Geiger, A.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
Motivated by the limitations of existing multi-view stereo benchmarks, we present a novel dataset for this task. Towards this goal, we recorded a variety of indoor and outdoor scenes using a high-precision laser scanner and captured both high-resolution DSLR imagery as well as synchronized low-resolution stereo videos with varying fields-of-view. To align the images with the laser scans, we propose a robust technique which minimizes photometric errors conditioned on the geometry. In contrast to previous datasets, our benchmark provides novel challenges and covers a diverse set of viewpoints and scene types, ranging from natural scenes to man-made indoor and outdoor environments. Furthermore, we provide data at significantly higher temporal and spatial resolution. Our benchmark is the first to cover the important use case of hand-held mobile devices while also providing high-resolution DSLR camera images. We make our datasets and an online evaluation server available at http://www.eth3d.net.

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pdf suppmat Project Page Project Page [BibTex]

pdf suppmat Project Page Project Page [BibTex]


Detailed, accurate, human shape estimation from clothed {3D} scan sequences
Detailed, accurate, human shape estimation from clothed 3D scan sequences

Zhang, C., Pujades, S., Black, M., Pons-Moll, G.

In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE Computer Society, Washington, DC, USA, IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), July 2017, Spotlight (inproceedings)

Abstract
We address the problem of estimating human body shape from 3D scans over time. Reliable estimation of 3D body shape is necessary for many applications including virtual try-on, health monitoring, and avatar creation for virtual reality. Scanning bodies in minimal clothing, however, presents a practical barrier to these applications. We address this problem by estimating body shape under clothing from a sequence of 3D scans. Previous methods that have exploited statistical models of body shape produce overly smooth shapes lacking personalized details. In this paper we contribute a new approach to recover not only an approximate shape of the person, but also their detailed shape. Our approach allows the estimated shape to deviate from a parametric model to fit the 3D scans. We demonstrate the method using high quality 4D data as well as sequences of visual hulls extracted from multi-view images. We also make available a new high quality 4D dataset that enables quantitative evaluation. Our method outperforms the previous state of the art, both qualitatively and quantitatively.

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arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]

arxiv_preprint video dataset pdf supplemental DOI Project Page [BibTex]


{3D} Menagerie: Modeling the {3D} Shape and Pose of Animals
3D Menagerie: Modeling the 3D Shape and Pose of Animals

Zuffi, S., Kanazawa, A., Jacobs, D., Black, M. J.

In Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017, pages: 5524-5532, IEEE, Piscataway, NJ, USA, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017 (inproceedings)

Abstract
There has been significant work on learning realistic, articulated, 3D models of the human body. In contrast, there are few such models of animals, despite many applications. The main challenge is that animals are much less cooperative than humans. The best human body models are learned from thousands of 3D scans of people in specific poses, which is infeasible with live animals. Consequently, we learn our model from a small set of 3D scans of toy figurines in arbitrary poses. We employ a novel part-based shape model to compute an initial registration to the scans. We then normalize their pose, learn a statistical shape model, and refine the registrations and the model together. In this way, we accurately align animal scans from different quadruped families with very different shapes and poses. With the registration to a common template we learn a shape space representing animals including lions, cats, dogs, horses, cows and hippos. Animal shapes can be sampled from the model, posed, animated, and fit to data. We demonstrate generalization by fitting it to images of real animals including species not seen in training.

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pdf video Project Page [BibTex]

pdf video Project Page [BibTex]