HOWTransfer recovers 3D hand motion from video, localizes contact intervals via hand-object cues, generates multi-modal grasp hypotheses, and edits trajectories to produce diverse robot-executable motions achieving 86% success.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.
citing papers explorer
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Hand-centric Human-to-Robot Trajectory Transfer from Video Demonstrations via Open-World Contact Localization
HOWTransfer recovers 3D hand motion from video, localizes contact intervals via hand-object cues, generates multi-modal grasp hypotheses, and edits trajectories to produce diverse robot-executable motions achieving 86% success.
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KPGrasp: Scalable Keypoint Flow Matching for Dexterous Grasp Generation
KPGrasp is a scalable Transformer flow-matching model using 3D hand keypoints that achieves 76.3% success on Dexonomy (47.4% improvement) and best average on DexGrasp Anything without contact losses or test-time refinement.