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STAR achieves superior interaction recognition by seamlessly integrating skeletal and visual data, enabling efficient inference on skeletons alone.
VideoLLMs may excel at recognizing actions but often hallucinate human motions, revealing a critical flaw in their design.
Visual goal prototypes can boost robot manipulation success rates by up to 17% compared to text-based instructions, revealing the power of leveraging action-free demonstrations.
Unlock the power of MLLMs for structured data like human skeletons with a differentiable rendering approach that allows end-to-end training.
