Learning Dynamic Grasping
Synthesizing believable 4D hand-object grasping sequences is important for character animation, extended reality, and embodied agents.
ProofPaper ↗
Key points
- We instead study the underconstrained problem of generating a complete grasping motion from a single initial hand-object state.
- Our framework decouples hand-motion generation from object-motion recovery.
- Given an object point cloud in the initial wrist frame and the initial parameterized hand configuration, conditional flow matching predicts a sequence-level contact map and a hand pose at each normalized sequence time.
- We then infer an attachment frame and recover rigid-object motion through training-free, contact-guided weighted registration.
Sources (1)
- [1]Learning Dynamic GraspingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 12:06 PM
Synthesizing believable 4D hand-object grasping sequences is important for character animation, extended reality, and embodied agents.
We instead study the underconstrained problem of generating a complete grasping motion from a single initial hand-object state.
Extractive summary: sentences quoted from the sources.
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