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Articulated 3D reconstruction has valuable applications in various domains,yet it remains costly and demands intensive work from domain experts. Recentadvancements in template-free learning methods show promising results withmonocular videos. Nevertheless, these approaches necessitate a comprehensivecoverage of all viewpoints of the subject in the input video, thus limitingtheir applicability to casually captured videos from online sources. In thiswork, we study articulated 3D shape reconstruction from a single and casuallycaptured internet video, where the subject's view coverage is incomplete. Wepropose DreaMo that jointly performs shape reconstruction while solving thechallenging low-coverage regions with view-conditioned diffusion prior andseveral tailored regularizations. In addition, we introduce a skeletongeneration strategy to create human-interpretable skeletons from the learnedneural bones and skinning weights. We conduct our study on a self-collectedinternet video collection characterized by incomplete view coverage. DreaMoshows promising quality in novel-view rendering, detailed articulated shapereconstruction, and skeleton generation. Extensive qualitative and quantitativestudies validate the efficacy of each proposed component, and show existingmethods are unable to solve correct geometry due to the incomplete viewcoverage.

DreaMo: Articulated 3D Reconstruction From A Single Casual Video