Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images
Quick summary
arXiv:2510.06145v2 Announce Type: replace-cross Abstract: 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 effectiv
Key takeaways
- arXiv:2510.06145v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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