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Human reconstruction and synthesis from monocular RGB videos is a challengingproblem due to clothing, occlusion, texture discontinuities and sharpness, andframespecific pose changes. Many methods employ deferred rendering, NeRFs andimplicit methods to represent clothed humans, on the premise that mesh-basedrepresentations cannot capture complex clothing and textures from RGB,silhouettes, and keypoints alone. We provide a counter viewpoint to thisfundamental premise by optimizing a SMPL+D mesh and an efficient,multi-resolution texture representation using only RGB images, binarysilhouettes and sparse 2D keypoints. Experimental results demonstrate that ourapproach is more capable of capturing geometric details compared to visualhull, mesh-based methods. We show competitive novel view synthesis andimprovements in novel pose synthesis compared to NeRF-based methods, whichintroduce noticeable, unwanted artifacts. By restricting the solution space tothe SMPL+D model combined with differentiable rendering, we obtain dramaticspeedups in compute, training times (up to 24x) and inference times (up to192x). Our method therefore can be used as is or as a fast initialization toNeRF-based methods.

Mesh Strikes Back: Fast and Efficient Human Reconstruction from RGB videos