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Even state-of-the-art streaming VLMs miss crucial fast-paced events in real-world video, scoring below 51% on a new high-dynamic benchmark.
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.
Fysiverse-3D-Vision is proposed, a unified vision-language-geometry framework for generative 3D scene reconstruction and executable asset construction from a single image that establishes a shared representation where spatial reasoning and geometric reconstruction mutually enhance each other, allowing object layouts to be inferred beyond the constraints of individual asset generators.
DreamHand achieves a groundbreaking 40% reduction in error for 3D hand trajectory recovery in occluded environments, setting a new standard for egocentric video analysis.
