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Why It Matters
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence.
Provenance
Discovered via ArXiv and published by ArXiv.
Key Claims
Original description
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer articulation from a single observation and therefore rely heavily on learned category-level shape priors. We present FAMOS, a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds. Our model jointly reasons over multiple observations and naturally supports a variable number of inputs, including a single view. To aggregate articulation cues...
Discovered via ArXiv
Research papers and preprints from arXiv.
Publisher: arxiv.org
ID: http://arxiv.org/abs/2609.20817v1 · Indexed about 1 hour ago