Subjects in self-contact

• TRAIN set: 4 subjects
• TEST set: 2 subjects

In each recording, the subject is motion tracked with a marker-based motion capture system (Vicon).

Multiple cameras

• 4 different views
• 900x900 resolution
• 50 fps
• Camera parameters:
  • extrinsics
  • intrinsics for 2 different camera models (one assuming image distortion, one ignoring it)
• The TEST set consists of only one random frontal camera viewpoint per sequence, to avoid simplifying the 3D Reconstruction challenge through multi-view triangulation/optimization.

Various self-contact actions in various scenarios

Each subject performs the following actions.

• standing (116 scenarios)
• sitting on the floor (20 scenarios)
• interacting with a chair (36 scenarios)

GHUM and SMPLX meshes

• Ground-truth, well-alligned mesh - obtained by fitting the GHUM model to accurate 3d markers, multi-view image evidence and body scans
• We retarget the GHUM meshes to the SMPLX topology and provide pose and shape parameters for both
• 50 fps

3D skeletons

• Ground-truth 3d skeletons with 25 joints (including the 17 Human3.6m joints)
• 50 fps

Self-contact annotations

Each of the 1032 recordings contain:

• 1 video timestamp where self-contact is established
• the self-contact signature annotation (multiple correspondences on the body surface between two triangle ids / vertex ids / region ids)
• the self-contact image support annotation (the projection of each self-contact vertex correspondence in the image)

Due to the 4 viewpoints, this amounts to 4128 triplets of images, self-contact signatures and self-contact image support.