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FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds, is presented and a procedural data generator that synthesizes self-annotated assets during training is introduced to overcome the limited scale and diversity of existing datasets.
Achieving state-of-the-art 4D reconstruction, this method transforms monocular videos into high-quality dynamic 3D representations, even in challenging conditions.
Video Language Models can achieve up to 86% faster time-to-first-token and 93% token reduction by ditching full-image encoding in favor of motion vectors and residuals from video codecs.
