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Ditching brittle hand-estimator-plus-SLAM cascades for an end-to-end streaming memory network slashes world-space hand tracking error by 21.4% while doubling inference throughput.
VideoMM is introduced, which marks a paradigm shift from model-centric downsizing to adaptive perceptual granularity and accelerates inference by 2.73 times over current leading methods, establishing a highly scalable paradigm for long-video understanding.