MonoArt: Progressive Structural Reasoning for Monocular Articulated 3D Reconstruction

Summary

MonoArt is a unified framework for articulated 3D reconstruction from a single image, presented by Haitian Li, Haozhe Xie, and Junxiang Xu (arXiv:2503.16922). Instead of directly predicting articulation parameters from image features, the framework applies progressive structural reasoning: it progressively transforms visual observations into canonical geometry, structured part representations, and motion-aware embeddings within a single architecture. According to the paper's abstract, this staged pipeline enables MonoArt to achieve state-of-the-art performance on PartNet-Mobility in both reconstruction accuracy and inference speed. The work falls in the computer vision domain and offers a single-architecture alternative to prior approaches that jump directly from appearance to articulation estimation, potentially improving robustness and efficiency for articulated object understanding. The original post was shared on zhichai.net with a Chinese-language summary alongside the English abstract; the paper is available on arXiv under ID 2503.16922.