[Paper] MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction

Source: arXiv - 2603.19231v1

Overview

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.

Key Contributions

This paper presents research in the following areas:

• cs.CV

Methodology

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Practical Implications

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Authors

• Haitian Li
• Haozhe Xie
• Junxiang Xu
• Beichen Wen
• Fangzhou Hong
• Ziwei Liu

Paper Information

• arXiv ID: 2603.19231v1
• Categories: cs.CV
• Published: March 19, 2026
• PDF: Download PDF