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
Datasets and Evaluation
Datasets with ground truth have driven many of the recent advances in computer vision. They allow evaluation and comparison so the field knows what works. They also provide training data to machine learning methods that are hungry for data. Creating good datasets that are valuable to the community and have a reasonable lifespan is hard work. Key issues are the quality and quantity of the data, how well that data addresses a specific problem in the field, and whether it is well curated with a good evaluation.
We have played central roles in many influential datasets and evaluations in the field including
- Middlebury flow dataset
- MPI-Sintel flow and related datasets
- KITTI datasets
- HumanEva for human pose estimation
- FAUST for 3D mesh registration
- JHMDB for action recognition
- MPI-I human pose dataset
Note that Perceiving Systems is involved in all three of the standard benchmarks in optical flow (Middlebury, KITTI and Sintel).
We are committed to releasing data whenever possible including
- Dyna: 40,000 4D human body scans
- Motion capture of extreme human poses