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Research Overview

Inferring and exploiting contact

Generative Proxemics: A Prior for 3D Social Interaction from Images

BITE -- Dog Shape and Pose from an Image

HOLD -- inferring 3D hand and object shape from video

MOVER -- Reconstructing 3D Scenes and People using Interaction

Datasets for understanding humans and animals

The Poses for Equine Research Dataset (PFERD)

BEAT2 Dataset for Holistic Co-Speech Gesture Generation

ARCTIC: A Dataset for Dexterous Bimanual Hand-Object Manipulation

The BioAMASS Dataset

OpenCapBench dataset

Human health and the 3D body

Body Shape Models in Treating Anorexia Nervosa

Customized Bone Plants for Humerus Shaft Fractures

Reconstructing Signing Avatars From Video Using Linguistic Priors

The AI animator

HAAR: Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles

Gaussian Garments

PuzzleAvatar: Assembling 3D Avatars from Personal Albums

FLARE: Fast Learning of Animatable and Relightable Mesh Avatars

Language, Vision, and World Models

AWOL: Analysis WithOut synthesis using Language

Re-Thinking Inverse Graphics with Large Language Models

TeCH: Text-guided Reconstruction of Clothed Humans

Human pose, shape, and motion capture

WHAM: Reconstructing World-grounded Humans with Accurate 3D Motion

3D Human Pose Estimation via Intuitive Physics

Accurate 3D Body Shape Regression using Metric and Semantic Attributes

BEV

Generating human motion

Generating Human Interaction Motions in Scenes with Text Control

TEMOS: Generating Diverse Human Motions from Text

EMAGE: Full-body Gestures from Audio

TEACH: Temporal Action Compositions for 3D Humans

Robot Perception Group

AirCap: 3D Motion Capture

AirCap: Perception-Based Control

AirCapRL: Aerial Motion Capture Using Deep RL

Data Team

Lab Tours and Public Outreach

Collecting Data - From the Idea to the Publication

Capture Technologies Setup

Completed Projects

Human Pose, Shape and Action

3D Pose from Images

2D Pose from Images

Beyond Motion Capture

Action and Behavior

Body Perception

Body Applications

Pose and Motion Priors

Clothing Models (2011-2015)

Reflectance Filtering

Learning on Manifolds

Markerless Animal Motion Capture

Multi-Camera Capture

2D Pose from Optical Flow

Body Perception

Neural Prosthetics and Decoding

Part-based Body Models

Intrinsic Depth

Lie Bodies

Layers, Time and Segmentation

Understanding Action Recognition (JHMDB)

Intrinsic Video

Intrinsic Images

Action Recognition with Tracking

Neural Control of Grasping

Flowing Puppets

Faces

Deformable Structures

Model-based Anthropometry

Modeling 3D Human Breathing

Optical flow in the LGN

FlowCap

Smooth Loops from Unconstrained Video

PCA Flow

Efficient and Scalable Inference

Motion Blur in Layers

Facade Segmentation

Smooth Metric Learning

Robust PCA

3D Recognition

Object Detection

Clothing Capture and Modeling

Modeling Human Movement

Action and Behavior

Differentiable Rendering

Image Segmentation and Semantics

Groups and Crowds

Learning from Synthetic Data

Learning Deep Representations of 3D

Efficient and Scalable Inference

Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

Learning to Resolve Intersections in Neural Multi-Garment Simulations

Reconstructing Simulation-Ready Clothing with Photo-Realistic Appearance from Multi-View Video

Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles

Human Hair Reconstruction with Strand-Aligned 3D Gaussians

Joker: Conditional 3D Head Synthesis with Extreme Facial Expressions

Goal Driven Motion Generation

Perceiving Systems Video Members Publications Website

Reconstructing Signing Avatars From Video Using Linguistic Priors

Sgnify
To capture 3D avatars from sign-language (SL) videos, we introduce novel linguistic priors that are universally applicable to SL and provide constraints on 3D hand pose that help resolve ambiguities within isolated signs [File Icon].
Sign language (SL) is the primary method of communication for the 70 million Deaf people around the world. Video dictionaries of isolated signs are a core SL learning tool. Replacing these with 3D avatars can aid learning and enable AR/VR applications, improving access to technology and online media. However, little work has attempted to estimate expressive 3D avatars from SL video. This task is difficult due to occlusion, noise, and motion blur. We address this by introducing novel linguistic priors that are universally applicable to SL and provide constraints on 3D hand pose that help resolve ambiguities within isolated signs. Our method, SGNify, captures fine-grained hand pose, facial expression, and body movement fully automatically from in-the-wild monocular SL videos. We evaluate SGNify quantitatively by using a commercial motion-capture system to compute 3D avatars synchronized with monocular video. SGNify outperforms state-of-the-art 3D body-pose- and shape-estimation methods on SL videos. A perceptual study shows that SGNify’s 3D reconstructions are significantly more comprehensible and natural than those of previous methods and are on par with the source videos.
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Video

Members

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Haptic Intelligence, Perceiving Systems
Maria-Paola Forte
  • Postdoctoral Researcher
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Perceiving Systems
Michael Black
Director
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Perceiving Systems
Peter Kulits
  • Doctoral Researcher
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Perceiving Systems
Chun-Hao Paul Huang
  • Guest Scientist
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Perceiving Systems
Vassilis Choutas
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Perceiving Systems
Dimitris Tzionas
  • Guest Scientist
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Haptic Intelligence
Katherine J. Kuchenbecker
Director

Publications

Haptic Intelligence Perceiving Systems Conference Paper Reconstructing Signing Avatars from Video Using Linguistic Priors Forte, M., Kulits, P., Huang, C. P., Choutas, V., Tzionas, D., Kuchenbecker, K. J., Black, M. J. In IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 12791-12801, CVPR, June 2023 (Published) pdf arXiv project code DOI URL BibTeX