SOURCE-LINKED INTELLIGENCE

FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations

arXiv · Artificial Intelligence · article · Sep 17, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global