mlsanityData to 14 Oct 2026

How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions

arXiv 2504.12284 ↗First posted 16 Apr 2025cs.CV cs.AI cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

We tackle the novel problem of predicting 3D hand motion and contact maps (or Interaction Trajectories) given a single RGB view, action text, and a 3D contact point on the object as input. Our approach consists of (1) Interaction Codebook: a VQVAE model to learn a latent codebook of hand poses and contact points, effectively tokenizing interaction trajectories, (2) Interaction Predictor: a transformer-decoder module to predict the interaction trajectory from test time inputs by using an indexer module to retrieve a latent affordance from the learned codebook. To train our model, we develop a data engine that extracts 3D hand poses and contact trajectories from the diverse HoloAssist dataset. We evaluate our model on a benchmark that is 2.5-10X larger than existing works, in terms of diversity of objects and interactions observed, and test for generalization of the model across object categories, action categories, tasks, and scenes. Experimental results show the effectiveness of our approach over transformer & diffusion baselines across all settings.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 45 of its 55 references that are in the map. First posted on arXiv 16 Apr 2025.

Builds on

45 of 55 references.

References · 45

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33 more
  1. 2023Reconstructing Hands in 3D with Transformerscited by 306
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  18. 2024DiffH2O: Diffusion-Based Synthesis of Hand-Object Interactions from Textual Descriptionscited by 62
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  33. 2023Mitigating Perspective Distortion-induced Shape Ambiguity in Image Cropscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 4.

10 papers.