MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction
Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi-view supervision, retrieval-based assembly, or auxiliary video generation, often sacrificing scalability or efficiency. We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. This structured reasoning process enables stable and interpretable articulation inference without external motion templates or multi-stage pipelines. Extensive experiments on PartNet-Mobility demonstrate that OM achieves state-of-the-art performance in both reconstruction accuracy and inference speed. The framework further generalizes to robotic manipulation and articulated scene reconstruction.
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Haitian Li
claimed_verified
2026-03-23T10:52:13.816Z
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Haitian Li
Marvin123123
user
Marvin123123
69bce3b2081b9ad49af57341
Haozhe Xie
claimed_verified
2026-04-01T08:36:40.804Z
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Haozhe Xie
hzxie
user
hzxie
69bce3b2081b9ad49af57342
Junxiang Xu
69bce3b2081b9ad49af57343
Beichen Wen
claimed_verified
2026-03-23T10:52:11.770Z
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Wen Beichen
wenbc21
user
wenbc21
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Fangzhou Hong
69bce3b2081b9ad49af57345
Ziwei Liu
MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction
Reconstructing articulated 3D objects from a single image requires jointly inferring object geometry, part structure, and motion parameters from limited visual evidence. A key difficulty lies in the entanglement between motion cues and object structure, which makes direct articulation regression unstable. Existing methods address this challenge through multi-view supervision, retrieval-based assembly, or auxiliary video generation, often sacrificing scalability or efficiency. We present MonoArt, a unified framework grounded in progressive structural reasoning. Rather than predicting articulation directly from image features, MonoArt progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. This structured reasoning process enables stable and interpretable articulation inference without external motion templates or multi-stage pipelines. Extensive experiments on PartNet-Mobility demonstrate that OM achieves state-of-the-art performance in both reconstruction accuracy and inference speed. The framework further generalizes to robotic manipulation and articulated scene reconstruction.
MonoArt presents a unified framework for reconstructing articulated 3D objects from single images through progressive structural reasoning that enables stable articulation inference without external templates or multi-stage processes.
articulated 3D objects visual observations canonical geometry structured part representations motion-aware embeddings progressive structural reasoning articulation inference PartNet-Mobility robotic manipulation articulated scene reconstruction