A Unified Framework
Self-occlusion is common when capturing people in the
wild, where the performers 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.
