Events & Talks

Perceiving Systems Talk Dr. Gerard Pons-Moll 30-11-2017 Reconstructing and Perceiving Humans in Motion For man-machine interaction it is crucial to develop models of humans that look and move indistinguishably from real humans. Such virtual humans will be key for application areas such as computer vision, medicine and psychology, virtual and augmented reality and special effects in movies. Currently, digital models typically lack realistic soft tissue and clothing or require time-consuming manual editing of physical simulation parameters. Our hypothesis is that better and more realistic models of humans and clothing can be learned directly from real measurements coming from 4D scans, ima... Melanie Feldhofer

Perceiving Systems Symposium 29-10-2017 - 01-11-2017 Scenes from Video III This is the third in a series of invitation-only workshops held after ICCV. SfV brings together experts on image and video understanding, machine learning, and 3D scene analysis. In so doing, we hope to draw several lines of research together to address the problem of extracting both physical and semantic information from video. Michael Black

Perceiving Systems Talk Anton Van Den Hengel 02-10-2017 Visual Question Answering and why we’re asking the wrong question Visual Question Answering is one of the applications of Deep Learning that is pushing towards real Artificial Intelligence. It turns the typical deep learning process around by only defining the task to be carried out after the training has taken place, which changes the task fundamentally. We have developed a range of strategies for incorporating other information sources into deep learning-based methods, and the process taken a step towards developing algorithms which learn how to use other algorithms to solve a problem, rather than solving it directly. This talk thus covers some of the ... Siyu Tang

Perceiving Systems Talk Yeara Kozlov 23-08-2017 Physical Blendshapes - Controllable Physics for Human Faces Creating convincing human facial animation is challenging. Face animation is often hand-crafted by artists separately from body motion. Alternatively, if the face animation is derived from motion capture, it is typically performed while the actor is relatively still. Recombining the isolated face animation with body motion is non-trivial and often results in uncanny results if the body dynamics are not properly reflected on the face (e.g. cheeks wiggling when running). In this talk, I will discuss the challenges of human soft tissue simulation and control. I will then present our method ... Timo Bolkart

Perceiving Systems Talk Seong Joon Oh 22-06-2017 From understanding to controlling privacy against automatic person identification Growth of the internet and social media has spurred the sharing and dissemination of personal data at large scale. At the same time, recent developments in computer vision has enabled unseen effectiveness and efficiency in automated recognition. It is clear that visual data contains private information that can be mined, yet the privacy implications of sharing such data have been less studied in computer vision community. In the talk, I will present some key results from our study of the implications of the development of computer vision on the identifiability in social media, and an analys... Siyu Tang

Perceiving Systems Talk Nadine Rüegg 06-06-2017 From Camera Synchronization to Deep Learning We transfer a monocular motion stereo 3D reconstruction algorithm from a mobile device (Google Project Tango Tablet) to a rigidly mounted external camera of higher image resolution. A reliable camera synchronization is crucial for the usability of the tablets IMU data and thus a time synchronization method developed. It is based on the joint movement of the cameras. In a second project, we move from outdoor video scenes to aerial images and strive to segment them into polygonal shapes. While most existing approaches address the problem of automated generation of online maps as a pixel-wise... Siyu Tang

Perceiving Systems Talk Partha Ghosh 31-05-2017 Human Motion Models We propose a new architecture for the learning of predictive spatio-temporal motion models from data alone. Our approach, dubbed the Dropout Autoencoder LSTM, is capable of synthesizing natural looking motion sequences over long time horizons without catastrophic drift or mo- tion degradation. The model consists of two components, a 3-layer recurrent neural network to model temporal aspects and a novel auto-encoder that is trained to implicitly recover the spatial structure of the human skeleton via randomly removing information about joints during train- ing time. This Dropout Autoe... Gerard Pons-Moll

Perceiving Systems Talk Endri Dibra 30-05-2017 3D shape from monocular images with data-driven priors Estimating 3D shape from monocular 2D images is a challenging and ill-posed problem. Some of these challenges can be alleviated if 3D shape priors are taken into account. In the field of human body shape estimation, research has shown that accurate 3D body estimations can be achieved through optimization, by minimizing error functions on image cues, such as e.g. the silhouette. These methods though, tend to be slow and typically require manual interactions (e.g. for pose estimation). In this talk, we present some recent works that try to overcome such limitations, achieving interactive rate... Gerard Pons-Moll

Perceiving Systems Talk Sven Dickinson 29-05-2017 The Perceptual Advantage of Symmetry for Scene Perception Human observers can classify photographs of real-world scenes after only a very brief exposure to the image (Potter & Levy, 1969; Thorpe, Fize, Marlot, et al., 1996; VanRullen & Thorpe, 2001). Line drawings of natural scenes have been shown to capture essential structural information required for successful scene categorization (Walther et al., 2011). Here, we investigate how the spatial relationships between lines and line segments in the line drawings affect scene classification. In one experiment, we tested the effect of removing either the junctions or the middle segments between juncti... Ahmed Osman

Perceiving Systems Talk Yael Moses 24-05-2017 Dynamic Scene Analysis Using CrowdCam Data Dynamic events such as family gatherings, concerts or sports events are often photographed by a group of people. The set of still images obtained this way is rich in dynamic content. We consider the question of whether such a set of still images, rather the traditional video sequences, can be used for analyzing the dynamic content of the scene. This talk will describe several instances of this problem, their solutions and directions for future studies. In particular, we will present a method to extend epipolar geometry to predict location of a moving feature in CrowdCam images. The method ... Jonas Wulff

Perceiving Systems Talk Cordelia Schmid 19-05-2017 Learning to segment moving objects This talk addresses the task of segmenting moving objects in unconstrained videos. We introduce a novel two-stream neural network with an explicit memory module to achieve this. The two streams of the network encode spatial and temporal features in a video sequence respectively, while the memory module captures the evolution of objects over time. The module to build a “visual memory” in video, i.e., a joint representation of all the video frames, is realized with a convolutional recurrent unit learned from a small number of training video sequences. Given video frames as input, our approach... Osman Ulusoy

Perceiving Systems Talk Björn Andres 08-05-2017 Graph Decomposition Problems in Image Analysis A large part of image analysis is about breaking things into pieces. Decompositions of a graph are a mathematical abstraction of the possible outcomes. This talk is about optimization problems whose feasible solutions define decompositions of a graph. One example is the correlation clustering problem whose feasible solutions relate one-to-one to the decompositions of a graph, and whose objective function puts a cost or reward on neighboring nodes ending up in distinct components. This talk shows applications of this problem and proposed generalizations to diverse image analysis tasks. It sk... Christoph Lassner

Perceiving Systems Talk Gul Varol 04-05-2017 Learning from Synthetic Humans Estimating human pose, shape, and motion from images and video 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: 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 m... Dimitris Tzionas

Perceiving Systems Talk Yanxi Liu 13-04-2017 Dancing with TURKs or Tai Chi with a Master? From gait, dance to martial art, human movements provide rich, complex yet coherent spatiotemporal patterns reflecting characteristics of a group or an individual. We develop computer algorithms to automatically learn such quality discriminative features from multimodal data. In this talk, I present a trilogy on learning from human movements: (1) Gait analysis from video data: based on frieze patterns (7 frieze groups), a video sequence of silhouettes is mapped into a pair of spatiotemporal patterns that are near-periodic along the time axis. A group theoretical analysis of periodic pat... Laura Sevilla Siyu Tang

Perceiving Systems Talk Silvia Zuffi 07-04-2017 Building Multi-Family Animal Models There has been significant prior work on learning realistic, articulated, 3D statistical shape models of the human body. In contrast, there are few such models for animals, despite their many applications in biology, neuroscience, agriculture, and entertainment. The main challenge is that animals are much less cooperative subjects 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. In the talk I will illustrate how we extend a state-of-the-art articulated 3D human body model (SMPL) to animals ...

Perceiving Systems Talk Kathleen Robinette 05-12-2016 Modeling Opportunities for Effective Product Development & Sizing Kathleen is the creator of the well-known CAESAR anthropomorphic dataset and is an expert on body shape and apparel fit. Javier Romero

Perceiving Systems Talk Hedvig Kjellström 27-10-2016 Factorized Latent Representations for Improved Automated Diagnostics In this talk I will first outline my different research projects. I will then focus on one project with applications in Health, and introduce the Inter-Battery Topic Model (IBTM). Our approach extends traditional topic models by learning a factorized latent variable representation. The structured representation leads to a model that marries benefits traditionally associated with a discriminative approach, such as feature selection, with those of a generative model, such as principled regularization and ability to handle missing data. The factorization is provided by representing data in ter...

Perceiving Systems Talk Siyu Tang 25-08-2016 Graph decomposition for multi-person tracking, pose estimation and motion segmentation Understanding people in images and videos is a problem studied intensively in computer vision. While continuous progress has been made, occlusions, cluttered background, complex poses and large variety of appearance remain challenging, especially for crowded scenes. In this talk, I will explore the algorithms and tools that enable computer to interpret people's position, motion and articulated poses in the real-world challenging images and videos.More specifically, I will discuss an optimization problem whose feasible solutions define a decomposition of a given graph. I will highlight the a... Naureen Mahmood

Perceiving Systems Talk Dimitris Tzionas 04-08-2016 Capturing Hand-Object Interaction and Reconstruction of Manipulated Objects Hand motion capture with an RGB-D sensor gained recently a lot of research attention, however even most recent approaches focus on the case of a single isolated hand. We focus instead on hands that interact with other hands or with a rigid or articulated object. Our framework successfully captures motion in such scenarios by combining a generative model with discriminatively trained salient points, collision detection and physics simulation to achieve a low tracking error with physically plausible poses. All components are unified in a single objective function that can be optimized with st... Javier Romero

Perceiving Systems Talk Timo Bolkart 09-06-2016 Dynamic and Groupwise Statistical Analysis of 3D Faces The accurate reconstruction of facial shape is important for applications such as telepresence and gaming. It can be solved efficiently with the help of statistical shape models that constrain the shape of the reconstruction. In this talk, several methods to statistically analyze static and dynamic 3D face data are discussed. When statistically analyzing faces, various challenges arise from noisy, corrupt, or incomplete data. To overcome the limitations imposed by the poor data quality, we leverage redundancy in the data for shape processing. This is done by processing entire motion seq...

Perceiving Systems Talk Cordelia Schmid 21-04-2016 Pose-based human action recognition. In this talk we present some recent results on human action recognition in videos. We, first, show how to use human pose for action recognition. To this end we propose a new pose-based convolutional neural network descriptor for action recognition, which aggregates motion and appearance information along tracks of human body parts. Next, we present an approach for spatio-temporal action localization in realistic videos. The approach first detects proposals at the frame-level and then tracks high-scoring proposals in the video. Our tracker relies simultaneously on instance-level and class-le...

Perceiving Systems Talk Gül Varol 12-04-2016 Long-term Temporal Convolutions for Action Recognition Typical human actions such as hand-shaking and drinking last several seconds and exhibit characteristic spatio-temporal structure. Recent methods attempt to capture this structure and learn action representations with convolutional neural networks. Such representations, however, are typically learned at the level of single frames or short video clips and fail to model actions at their full temporal scale. In this work we learn video representations using neural networks with long-term temporal convolutions. We demonstrate that CNN models with increased temporal extents improve the accuracy ...

Perceiving Systems Talk Helge Rhodin 08-04-2016 Ray Tracing for Computer Vision Proper handling of occlusions is a big challenge for model based reconstruction, e.g. for multi-view motion capture a major difficulty is the handling of occluding body parts. We propose a smooth volumetric scene representation, which implicitly converts occlusion into a smooth and differentiable phenomena (ICCV2015). Our ray tracing image formation model helps to express the objective in a single closed-form expression. This is in contrast to existing surface(mesh) representations, where occlusion is a local effect, causes non-differentiability, and is difficult to optimize. We demon...

Perceiving Systems Talk Aamir Ahmad 05-04-2016 Multirobot Cooperative State Estimation - towards Scalability and Active Perception The core focus of my research is on robot perception. Within this broad categorization, I am mainly interested in understanding how teams of robots and sensors can cooperate and/or collaborate to improve the perception of themselves (self-localization) as well as their surroundings (target tracking, mapping, etc.). In this talk I will describe the inter-dependencies of such perception modules and present state-of-the-art methods to perform unified cooperative state estimation. The trade-off between accuracy of estimation and computational speed will be highlighted through a new optimization...

Perceiving Systems Talk Valsamis Ntouskos 04-04-2016 Regularization and Statistical Inverse Problems in Shape and Motion Modeling Modeling and reconstruction of shape and motion are problems of fundamental importance in computer vision. Inverse Problem theory constitutes a powerful mathematical framework for dealing with ill-posed problems as the ones typically arising in shape and motion modeling. In this talk, I will present methods inspired by Inverse Problem theory, for dealing with four different shape and motion modeling problems. In particular, in the context of shape modeling, I will present a method for component-wise modeling of articulated objects and its application in computing 3D models of anim...

Perceiving Systems Talk Lars Mescheder 03-03-2016 From image restoration to image understanding Inverse problems are ubiquitous in image processing and applied science in general. Such problems describe the challenge of computing the parameters that characterize a system from the outcomes. While this might seem easy at first for simple systems, many inverse problems share a property that makes them much more intricate: they are ill-posed. This means that either the problem does not have a unique solution or this solution does not depend continuously on the outcomes of the system. Bayesian statistics provides a framework that allows to treat such problems in a systematic way. The missi...

Perceiving Systems Talk Helga Griffiths 24-02-2016 Interaction of Science and Art In general Helga Griffiths is a Multi-Sense-Artist working on the intersection of science and art. She has been working for over 20 years on the integration of various sensory stimuli into her “multi-sense” installations. Typical for her work is to produce a sensory experience to transcend conventional boundaries of perception. Emma-Jayne Holderness

Perceiving Systems Talk Prof. David W. Jacobs 10-11-2015 Understanding Plants and Animals I will describe a series of work that aims to automatically understand images of animals and plants. I will begin by describing recent work that uses Bounded Distortion matching to model pose variation in animals. Using a generic 3D model of an animal and multiple images of different individuals in various poses, we construct a model that captures the way in which the animal articulates. This is done by solving for the pose of the template that matches each image while simultaneously solving for the stiffness of each tetrahedron of the model. We minimize an L1 norm on stiffness, produci... Stephan Streuber

Perceiving Systems Talk Olga Diamanti 28-10-2015 Design of Tangent Vector-Set Fields using Polynomials The design of tangent vector fields on discrete surfaces is a basic building block for many geometry processing applications, such as surface remeshing, parameterization and architectural geometric design. Many applications require the design of multiple vector fields (vector sets) coupled in a nontrivial way; for example, sets of more than two vectors are used for meshing of triangular, quadrilateral and hexagonal meshes. In this talk, a new, polynomial-based representation for general unordered vector sets will be presented. Using this representation we can efficiently interpolate user pr... Gerard Pons-Moll

Perceiving Systems Talk Max Welling 19-10-2015 Learning to generate The recent amazing success of deep learning has been mainly in discriminative learning, that is, classification and regression. An important factor for this success has been, besides Moore's law, the availability of large labeled datasets. However, it is not clear whether in the future the amount of available labels grows as fast as the amount of unlabeled data, providing one argument to be interested in unsupervised and semi-supervised learning. Besides this there are a number of other reasons why unsupervised learning is still important, such as the fact that data in the life sciences ... Peter Vincent Gehler

Perceiving Systems Talk Lilla LoCurto and Bill Outcalt 21-09-2015 "Artist" Talk Lilla and Bill are two returning artists to Perceiving Systems. Their talk will update us on the exciting projects that they’ve been involved with since their last visit and to present some of their current plans that will unfold during the week (Sept 21st - 25th). They will be joining our department and working with professional dancers in the 4D scanner as part of an art project on mental health. In general, Lilla and Bill have been using 3D captures as an artistic tool to visualize the human body in a contemporary form for some time. They produce marionettes or avatars which can be se... Emma-Jayne Holderness

Perceiving Systems Talk Irfan Essa 10-09-2015 Data-Driven Methods for Video Analysis and Enhancement In this talk, I will start with describing the pervasiveness of image and video content, and how such content is growing with the ubiquity of cameras. I will use this to motivate the need for better tools for analysis and enhancement of video content. I will start with some of our earlier work on temporal modeling of video, then lead up to some of our current work and describe two main projects. (1) Our approach for a video stabilizer, currently implemented and running on YouTube, and its extensions. (2) A robust and scaleable method for video segmentation. I will describe, in some deta... Naejin Kong

Perceiving Systems Talk Sergi Rocamora 08-09-2015 Bayesian Image-Based Rendering and Application to Stereoscopic Cinema and 3DTV Optics with long focal length have been extensively used for shooting 2D cinema and television, either to virtually get closer to the scene or to produce an aesthetical effect through the deformation of the perspective. However, in 3D cinema or television, the use of long focal length either creates a ``cardboard effect'' or causes visual divergence. To overcome this problem, state-of-the-art methods use disparity mapping techniques, which is a generalization of view interpolation, and generate new stereoscopic pairs from the two image sequences. We propose to use more than two cameras to s...

Perceiving Systems Talk Darren Cosker 02-09-2015 Applying Computer Vision and Graphics Research in Visual Effects and Entertainment The visual effects and entertainment industries are now a fundamental part of the computer graphics and vision landscapes - as well as impacting across society in general. One of the issues in this area is the creation of realistic characters, creating assets for production, and improving work-flow. Advances in computer graphics, vision and rendering have underlined much of the success of these industries, built on top of academic advances. However, there are still many unsolved problems. In this talk I will outline some of the challenges we have faced in crossing over academic research i... Silvia Zuffi

Perceiving Systems Talk Bojan Pepik 01-09-2015 Towards Richer Object Representations for Object Class Detection in Real World Images Current object class detection methods typically target 2D bounding box localization, encouraged by benchmark data sets, such as Pascal VOC. While this seems suitable for the detection of individual objects, higher-level applications, such as autonomous driving and 3D scene understanding, would benefit from more detailed and richer object hypotheses. In this talk I will present our recent work on building more detailed object class detectors, bridging the gap between higher level tasks and state-of-the-art object detectors. I will present a 3D object class detection method that can reliably...

Perceiving Systems Talk Luca del Pero 26-08-2015 Articulated motion discovery using pairs of trajectories Most computer vision systems cannot take advantage of the abundance of Internet videos as training data. This is because current methods typically learn under strong supervision and require expensive manual annotations. (e.g. videos need to be temporally trimmed to cover the duration of a specific action, object bounding boxes, etc.). In this talk, I will present two techniques that can lead to learning the behavior and the structure of articulated object classes (e.g. animals) from videos, with as little human supervision as possible. First, we discover the characteristic motion patterns o... Laura Sevilla

Perceiving Systems Talk Garrett Stanley 10-07-2015 Reading and Writing the Neural Code: Challenges in Neuroengineering The external world is represented in the brain as spatiotemporal patterns of electrical activity. Sensory signals, such as light, sound, and touch, are transduced at the periphery and subsequently transformed by various stages of neural circuitry, resulting in increasingly abstract representations through the sensory pathways of the brain. It is these representations that ultimately give rise to sensory perception. Deciphering the messages conveyed in the representations is often referred to as “reading the neural code”. True understanding of the neural code requires knowledge of not on... Jonas Wulff

Perceiving Systems Talk Trevor Darrell 26-06-2015 Perceptual representation learning across diverse modalities and domains Learning of layered or "deep" representations has provided significant advances in computer vision in recent years, but has traditionally been limited to fully supervised settings with very large amounts of training data. New results show that such methods can also excel when learning in sparse/weakly labeled settings across modalities and domains. I'll present our recent long-term recurrent network model which can learn cross-modal translation and can provide open-domain video to text transcription. I'll also describe state-of-the-art models for fully convolutional pixel-dense segmentati... Jonas Wulff

Perceiving Systems Talk Rich Zemel 10-06-2015 Learning Rich and Fair Representations from Images and Text I will talk about two types of machine learning problems, which are important but have received little attention. The first are problems naturally formulated as learning a one-to-many mapping, which can handle the inherent ambiguity in tasks such as generating segmentations or captions for images. A second problem involves learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. The primary approach we formulate for both problems is a constrained form of joint emb... Gerard Pons-Moll

Perceiving Systems Talk Hans-Peter Seidel 18-05-2015 3D Image Analysis and Synthesis -- The World inside the Computer During the last three decades computer graphics established itself as a core discipline within computer science and information technology. Two decades ago, most digital content was textual. Today it has expanded to include audio, images, video, and a variety of graphical representations. New and emerging technologies such as multimedia, social networks, digital television, digital photography and the rapid development of new sensing devices, telecommunication and telepresence, virtual reality, or 3D-internet further indicate the potential of computer graphics...

Perceiving Systems Talk Andrea Vedaldi 04-05-2015 Learning and understanding visual representations Learnable representations, and deep convolutional neural networks (CNNs) in particular, have become the preferred way of extracting visual features for image understanding tasks, from object recognition to semantic segmentation. In this talk I will discuss several recent advances in deep representations for computer vision. After reviewing modern CNN architectures, I will give an example of a state-of-the-art network in text spotting; in particular, I will show that, by using only synthetic data and a sufficiently large deep model, it is possible directly map image regions to Englis...

Perceiving Systems IS Colloquium Cristian Sminchisescu 24-03-2015 From Perceptual Evidence to Large-Scale Visual Recognition Models Recent progress in computer-based visual recognition heavily relies on machine learning methods trained using large scale annotated datasets. While such data has made advances in model design and evaluation possible, it does not necessarily provide insights or constraints into those intermediate levels of computation, or deep structure, perceived as ultimately necessary in order to design reliable computer vision systems. This is noticeable in the accuracy of state of the art systems trained with such annotations, which still lag behind human performance in similar tasks. Nor does the exist...

Perceiving Systems Talk Benedetta Gennaro 11-03-2015 Of Breasts and Symbols: A Visual Journey through Twenty-Five Centuries of Western Art and Culture The breast is not just a protruding gland situated on the front of the thorax in female bodies: behind biology lies an intricate symbolism that has taken various and often contradictory meanings.  We begin our journey looking at pre-historic artifacts that revered the breast as the ultimate symbol of life; we then transition to the rich iconographical tradition centering on the so-called Virgo Lactans when the breast became a metaphor of nourishment for the entire Christian community. Next, we look at how artists have eroticized the breast in portraits of fifteenth-century French court...

Perceiving Systems Talk Michael Tarr 26-02-2015 "Real stupidity beats artificial intelligence every time" (Terry Pratchett) How is it that biological systems can be so imprecise, so ad hoc, and so inefficient, yet accomplish (seemingly) simple tasks that still elude state-of-the-art artificial systems? In this context, I will introduce some of the themes central to CMU's new BrainHub Initiative by discussing: (1) The complexity and challenges of studying the mind and brain; (2) How the study of the mind and brain may benefit from considering contemporary artificial systems; (3) Why studying the mind and brain might be interesting (and possibly useful) to computer scientists.

Perceiving Systems Talk Paul G. Kry 24-02-2015 Balancing Speed and Fidelity in Physics Based Animation and Control In this talk I will give an overview of work I have done over the years exploring physically based simulation of contact, deformation, and articulated structures where there are trade-offs between computational speed and physical fidelity that can be made.  I will also discuss examples that mix data-driven and physically based approaches in animation and control.<br /> <br /> Paul Kry is an associate professor in the School of Computer Science at McGill University.  He has a BMath from University of Waterloo, and MSc and PhD from University of British Columbia.  His res...

Perceiving Systems Talk Nikolaus F. Troje 18-02-2015 What is biological motion? <p> Everyone in visual psychology seems to know what Biological Motion is. Yet, it is not easy to come up with a definition that is specific enough to justify a distinct label, but is also general enough to include the many different experiments to which the term has been applied in the past. I will present a number of tasks, stimuli, and experiments, including some of my own work, to demonstrate the diversity and the appeal of the field of biological motion perception. In trying to come up with a definition of the term, I will particularly focus on a type of motion that has been consider...

Perceiving Systems Talk Vladlen Koltun 17-02-2015 Reconstructing Complete 3D Models from Single Images We present an approach to creating 3D models of objects depicted in Web images, even when each object may only be shown in a single image. Our approach uses a comparatively small collection of existing 3D models to guide the reconstruction process. These existing shapes are used to derive information about shape structure. Our guiding idea is to jointly analyze the images and the available 3D models. Joint analysis of all images along with the available shapes regularizes the formulated optimization problems, stabilizes estimation of camera parameters and construction of dense pixel-level c...

Perceiving Systems IS Colloquium Michael Goesele 16-02-2015 Reflecting in and on the Gradient Domain Image-based rendering has been introduced in the 1990s as an alternative approach to photorealistic rendering. Its key idea is to novel renderings by re-projecting pixels from nearby views. The basic approach works well for many scenes but breaks down if the scene contains “non-standard” elements such as reflective surfaces. In this talk, I will first show how we can extend image-based rendering to handle scenes with reflections. I will then discuss a novel gradient-based technique for image-based rendering that can intrinsically handle scenes with reflections.</pre>

Perceiving Systems Symposium 10-12-2014 - 13-12-2014 Scenes from Videos Workshop This invitation-only workshop will bring together experts in the field to focus on the problem of estimating Scenes from Video. In so doing, we hope to draw several lines of research together to address the problem of extracting physical and semantic information from video.

Perceiving Systems Talk Wenzel Jakob 28-10-2014 Capturing and simulating the interaction of light with the world around us Driven by the increasing demand for photorealistic computer-generated images, graphics is currently undergoing a substantial transformation to physics-based approaches which accurately reproduce the interaction of light and matter. Progress on both sides of this transformation -- physical models and simulation techniques -- has been steady but mostly independent from another. When combined, the resulting methods are in many cases impracticably slow and require unrealistic workarounds to process even simple everyday scenes. My research lies at the interface of these two research fields; my g...