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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 Members Publications Website

Hierarchical Graphs for Generalized Modelling of Clothing Dynamics

Teasera hood
We combine graph neural networks, a hierarchical graph representation, and multi-level message passing with an unsupervised training scheme to enable efficient prediction of realistic clothing dynamics for arbitrary types of garments and body shapes. Our method models both tight-fitting and free-flowing clothes draped over arbitrary body shapes. At test time, the method generalizes to new, entirely unseen, garments (left), and allows dynamic and unconstrained poses (right) and changes in material parameters and topology.
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable efficient prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method, called HOOD, is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Furthermore, HOOD handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that HOOD outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.
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Members

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Perceiving Systems
Artur Grigorev
  • Guest Scientist
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Perceiving Systems
Michael Black
Emeritus / Acting Director

Publications

Perceiving Systems Conference Paper HOOD: Hierarchical Graphs for Generalized Modelling of Clothing Dynamics Grigorev, A., Thomaszewski, B., Black, M. J., Hilliges, O. In IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), 16965-16974, CVPR, June 2023 (Published)
We propose a method that leverages graph neural networks, multi-level message passing, and unsupervised training to enable real-time prediction of realistic clothing dynamics. Whereas existing methods based on linear blend skinning must be trained for specific garments, our method is agnostic to body shape and applies to tight-fitting garments as well as loose, free-flowing clothing. Our method furthermore handles changes in topology (e.g., garments with buttons or zippers) and material properties at inference time. As one key contribution, we propose a hierarchical message-passing scheme that efficiently propagates stiff stretching modes while preserving local detail. We empirically show that our method outperforms strong baselines quantitatively and that its results are perceived as more realistic than state-of-the-art methods.
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