Learning from Synthetic data
- Perceiving Systems
- Learning from Synthetic data
- Research Overview
- Modeling 3D Humans and Animals
- Human Pose, Shape, and Motion
- Behavior, Action, and Language
- Synthesizing People
- Society, Medicine, and Psychology
- Scenes, Structure and Motion
- Beyond Mocap
- Datasets
- Robot Perception Group
- Holistic Vision Group
- Data Team
-
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
Javier Romero,
Anurag Ranjan,
Michael Black (Director),
Jonas Wulff,
David Hoffmann,
Dimitris Tzionas,
Siyu Tang,
Naureen Mahmood,
Gul Varol,
Cordelia Schmid,
Gül Varol
(INRIA Paris),
Xavier Martin
(INRIA Grenoble Rhone-Alpes),
Ivan Laptev
(INRIA Paris),
Cordelia Schmid
(INRIA Grenoble Rhone-Alpes)
lala