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.
- Predicts movable-part segmentation and joint parameters from sparse partial point clouds
- Multi-state Articulation Transformer aggregates articulation cues across multiple observations
- Articulation span objective supervises motion ranges exhibited across inputs
- Procedural data generator addresses limited scale and diversity of articulation datasets
From the original article
1 min read at arxiv.orgModeling 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 · arxiv.org
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