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IPMAN: 3D Human Pose Estimation via Intuitive Physics
Abstract The estimation of 3D human body shape and pose from images has advanced rapidly. While the results are often well aligned with image features in the camera view, the 3D pose is often physically implausible; bodies lean, float, or penetrate the floor.
Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael Black, Dimitrios Tzionas
Cite PDF Video Code Data (MoYo) Poster Project CVPR2023
IPMAN: 3D Human Pose Estimation via Intuitive Physics
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction
Abstract Humans constantly interact with objects to accomplish tasks. To understand such interactions, computers need to reconstruct these in 3D from images of whole bodies manipulating objects, e.g., for grasping, moving, and using the latter.
Yinghao Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas
Cite PDF arXiv Video Code Data Poster Project GCPR2022
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
Abstract Generating digital humans that move realistically has many applications and is widely studied, but existing methods focus on the major limbs of the body, ignoring the hands and head. Hands have been separately studied but the focus has been on generating realistic static grasps of objects.
Omid Taheri, Vasileios Choutas, Michael J. Black, Dimitrios Tzionas
Cite PDF arXiv Video Code Poster Project CVPR2022
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Abstract Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time.
Omid Taheri, Nima Ghorbani, Michael J. Black, Dimitrios Tzionas
Cite arXiv Video GRAB GrabNet Data Project ECCV2020
GRAB: A Dataset of Whole-Body Human Grasping of Objects