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.
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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 across observations, we introduce a Multi-state Articulation Transformer with alternating state-wise and global attention. We further propose an observed articulation span objective that supervises the motion range each part exhibits across the input observations, encouraging the model to leverage the full observation set. To overcome the limited scale and diversity of existing datasets, we introduce a procedural data generator that synthesizes self-annotated assets during training. Experiments on PartNet-Mobility, ACD, and ArtiCraft-10K demonstrate consistent improvements over both feed-forward and optimization-based baselines. Project page: https://kevinqu7.github.io/famos
Text extracted automatically; images, tables and formatting may be missing. Original: https://huggingface.co/papers/2609.20817