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.
- Predicts movable-part segmentation and joint parameters from sparse unordered partial point clouds.
- Multi-state Articulation Transformer alternates state-wise and global attention across observations.
- Procedural generator synthesizes self-annotated training assets; improvements shown on PartNet-Mobility, ACD, ArtiCraft-10K.
From the original article
1 min read at huggingface.co
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.
By Kevin Qu, Tao Sun, Massimiliano Viola · huggingface.co
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