DreaMo: Articulated 3D Reconstruction from a Single Casual Video

Tao Tu, Ming-Feng Li, Chieh Hubert Lin, Yen-Chi Cheng, Min Sun, Ming-Hsuan Yang; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 2269-2279

Articulated 3D reconstruction has valuable applications in various domains yet it remains costly and demands intensive work from domain experts. Recent advancements in template-free learning methods show promising results with monocular videos. Nevertheless these approaches necessitate a comprehensive coverage of all viewpoints of the subject in the input video thus limiting their applicability to casually captured videos from online sources. In this work we study articulated 3D shape reconstruction from a single and casually captured Internet video where the subject's view coverage is incomplete. We propose DreaMo that jointly performs shape reconstruction while solving the challenging low-coverage regions with view-conditioned diffusion prior and several tailored regularizations. In addition we introduce a skeleton generation strategy to create human-interpretable skeletons from the learned neural bones and skinning weights without any predefined skeleton structures. We conduct our study on a self-collected internet video collection characterized by incomplete view coverage. DreaMo shows promising quality in novel-view rendering detailed articulated shape reconstruction and skeleton generation. Extensive qualitative and quantitative studies validate the efficacy of each proposed component and show existing methods are unable to solve correct geometry due to the incomplete view coverage.