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A massive 2.6M-scene benchmark and a novel generative model finally unlock robust dexterous grasping in cluttered environments, outperforming prior methods without post-optimization.
DreamHand achieves a groundbreaking 40% reduction in error for 3D hand trajectory recovery in occluded environments, setting a new standard for egocentric video analysis.