Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images
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Abstract (arXiv)
We tackle the problem of forecasting bimanual 3D hand motion and articulation from a single image in everyday settings. To address the lack of 3D hand annotations in diverse settings, we design an annotation pipeline consisting of a diffusion model to lift 2D hand keypoint sequences to 4D hand motion. For the forecasting model, we adopt a diffusion loss to account for the multimodality in hand motion distribution. Extensive experiments on 6 datasets show the benefits of training with our imputed labels (14% improvement) and the effectiveness of our lifting (45% better) & forecasting (16.4% gain) models, over the best baselines, especially in zero-shot generalization to everyday images.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 65 of its 79 references that are in the map. First posted on arXiv 7 Oct 2025.
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References · 65
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Citing papers per year: 2026: 1.
1 paper.
- 2026HandX: Scaling Bimanual Motion and Interaction Generation30 Mar 2026
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- 2025Text-Driven 3D Hand Motion Generation from Sign Language Data
- 2025Novel Diffusion Models for Multimodal 3D Hand Trajectory Prediction
- 2023Denoising Diffusion for 3D Hand Pose Estimation from Images
- 2024HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data
- 2026CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild
- 2026Teacher-Student Diffusion Model for Text-Driven 3D Hand Motion Generation
- 2023BOTH2Hands: Inferring 3D Hands from Both Text Prompts and Body Dynamics
- 2026EggHand: A Multimodal Foundation Model for Egocentric Hand Pose Forecasting