EPIC-Contact dataset and HOPformer bring 3D hand-object pose estimation into the wild
Michael_J_Black · x · 2026-09-10
University of Bristol and MPI researchers presented at ECCV 'Towards in-the-wild Egocentric 3D Hand-Object Pose Estimation': EPIC-Contact, an in-the-wild egocentric dataset (2.3K clips, 62.3K frames from EPIC-Kitchens) with dense bijective 3D hand-object contact annotations enabling supervision without motion capture; and HOPformer, a transformer conditioning object pose on strong hand priors to predict hands and object in a single forward pass under clutter and occlusion. Dataset, code, and checkpoints are released on arXiv/HF.
More from Embodied
- Full teardown: inside Zmotor's 32mm linear actuator for humanoids — MarwaEldiwiny · 2026-10-27
- Full Teardown of a Zmotor Linear Actuator Used in China's Humanoid Robots — MarwaEldiwiny · 2026-10-27
- World Model Assigns Weight and Friction to Objects, Then Simulates with a Physics Engine — jonstephens85 · 2026-10-27
- First unboxing shots of Apple's foldable "iPhone Duo" surface — chrisfirst · 2026-10-27
- Bedrock Robotics Raises $600M Series C as Autonomous Excavators Do Paid Work on US Job Sites — davidpattersonx · 2026-10-27
- UniAction: unified action conditioning lets one world model handle mixed action types — ZeYanjie · 2026-10-27