  Human reconstruction and synthesis from monocular RGB videos is a challenging
problem due to clothing, occlusion, texture discontinuities and sharpness, and
framespecific pose changes. Many methods employ deferred rendering, NeRFs and
implicit methods to represent clothed humans, on the premise that mesh-based
representations cannot capture complex clothing and textures from RGB,
silhouettes, and keypoints alone. We provide a counter viewpoint to this
fundamental premise by optimizing a SMPL+D mesh and an efficient,
multi-resolution texture representation using only RGB images, binary
silhouettes and sparse 2D keypoints. Experimental results demonstrate that our
approach is more capable of capturing geometric details compared to visual
hull, mesh-based methods. We show competitive novel view synthesis and
improvements in novel pose synthesis compared to NeRF-based methods, which
introduce noticeable, unwanted artifacts. By restricting the solution space to
the SMPL+D model combined with differentiable rendering, we obtain dramatic
speedups in compute, training times (up to 24x) and inference times (up to
192x). Our method therefore can be used as is or as a fast initialization to
NeRF-based methods.
