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. 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.

本研究提出了 DreaMo，一种从单个且视角覆盖不完整的网络视频中重建关节 3D 形状的方法，通过使用条件视图扩散先验和一些定制的正则化方法解决了低视角覆盖区域带来的挑战，并引入了骨架生成策略生成可解释的人体骨架。对自行收集的视角覆盖不完整的网络视频进行了广泛的定性和定量研究，结果表明 DreaMo 在新视角渲染、详细关节形状重建和骨架生成方面具有良好的质量，并且现有的方法由于视角覆盖不完整而无法解决正确的几何问题。