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About this paper
Chen Bao, Jiarui Xu, Xiaolong Wang, Abhinav Gupta, Homanga Bharadhwaj
HandsOnVLM, a video-based Vision-Language Model, predicts future 2D hand trajectories conditioned on natural language instructions, integrating high-level VLM reasoning with low-level auto-regressive action prediction. The model surpasses existing baselines on new language-conditioned benchmarks, achieving, for instance, an ADE of 0.187 on the Epic-Kitchen RBHP dataset.
Key insights
- Integrating high-level VLM reasoning with low-level auto-regressive trajectory prediction enables language-conditioned generation of precise physical actions.
- Training on a diverse set of tasks, including dense video captioning and video QA, alongside specific hand prediction tasks, enhances the model's general visual understanding and reasoning capabilities for trajectory prediction.
- The model demonstrates a capacity to reason about plausible future hand trajectories from implicit, colloquial language queries, moving beyond explicit action commands and leveraging world knowledge.
Summary by alphaXiv.
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