Self-occlusion is common when capturing people in the wild, where the performer do not follow predefined motion scripts. This challenges existing monocular human reconstruction systems that assume full body visibility. We introduce Self-Occluded Avatar Recovery (SOAR), a method for complete human reconstruction from partial observations where parts of the body are entirely unobserved. SOAR leverages structural normal prior and generative diffusion prior to address such an ill-posed reconstruction problem. For structural normal prior, we model human with an reposable surfel model with well-defined and easily readable shapes. For generative diffusion prior, we perform an initial reconstruction and refine it using score distillation. On various benchmarks, we show that SOAR performs favorably than state-of-the-art reconstruction and generation methods, and on-par comparing to concurrent works. Additional video results and code are available at https://soar-avatar.github.io/.

翻译：在野外拍摄人物时，自遮挡现象十分常见，因为表演者不会遵循预定义的动作脚本。这对现有的单目人体重建系统构成了挑战，因为这些系统通常假设身体完全可见。我们提出了自遮挡人体模型恢复方法SOAR，该方法能够从身体部分区域完全不可见的局部观测中实现完整的人体重建。SOAR利用结构法线先验和生成式扩散先验来解决这一病态重建问题。在结构法线先验方面，我们采用具有明确定义且易于读取形状的可重构成面元模型对人体进行建模。在生成式扩散先验方面，我们执行初始重建并通过分数蒸馏进行细化。在多个基准测试中，SOAR的表现优于当前最先进的重建与生成方法，并与同期研究工作性能相当。更多视频结果和代码可在 https://soar-avatar.github.io/ 获取。