HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.
In: CVPR (2023)
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MoCHA canonicalizes captions to motion-recoverable semantics before contrastive training, cutting within-motion embedding variance by 11-19% and lifting T2M R@1 by 3.1pp on HumanML3D and 10.3pp on KIT-ML.
citing papers explorer
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HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching
HO-Flow synthesizes realistic hand-object motions from text and canonical 3D objects via an interaction-aware VAE and masked flow matching, reporting SOTA physical plausibility and diversity on GRAB, OakInk, and DexYCB.
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MoCHA: Denoising Caption Supervision for Motion-Text Retrieval
MoCHA canonicalizes captions to motion-recoverable semantics before contrastive training, cutting within-motion embedding variance by 11-19% and lifting T2M R@1 by 3.1pp on HumanML3D and 10.3pp on KIT-ML.