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FAMOS

2 mentions in 7 days · 2 in 30 days · 2 total · first seen Sep 16, 2026 · last 22h ago

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

FAMOS is a feed-forward model predicting movable-part segmentation and joint parameters from sparse unordered point clouds, beating baselines on PartNet-Mobility, ACD, and ArtiCraft-10K.

FAMOS predicts articulated-object segmentation and joint parameters from a sparse, unordered set of partial monocular point clouds, jointly reasoning across a variable number of observations including a single view. It uses a Multi-state Articulation Transformer with alternating state-wise and global attention and an observed articulation span objective, plus a procedural generator that synthesizes self-annotated training assets. Experiments on PartNet-Mobility, ACD, and ArtiCraft-10K show consistent improvements over both feed-forward and optimization-based baselines.

arXiv cs.AI / cs.LG / cs.CLSep 17, 17:59 UTC · 22h agoAI research 2 sources

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

FAMOS is a feed-forward model predicting movable-part segmentation and joint parameters from sparse unordered point clouds, outperforming baselines on PartNet-Mobility and ACD.

FAMOS is a feed-forward model that predicts movable-part segmentation and joint parameters from a sparse, unordered set of partial point clouds, supporting a variable number of inputs including a single view. It introduces a Multi-state Articulation Transformer with alternating state-wise and global attention plus an observed articulation span objective. To overcome dataset limitations, the authors built a procedural generator that synthesizes self-annotated assets during training. Experiments on PartNet-Mobility, ACD, and ArtiCraft-10K show consistent improvements over both feed-forward and optimization-based baselines.

Hugging Face daily papersupdated Sep 17, 17:59 UTC · 22h agofirst Sep 16, 20:00 UTC · 1d agoAI research 2 sources

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