  We present a self-supervised human mesh recovery framework to infer human
pose and shape from monocular images in the absence of any paired supervision.
Recent advances have shifted the interest towards directly regressing
parameters of a parametric human model by supervising them on large-scale
datasets with 2D landmark annotations. This limits the generalizability of such
approaches to operate on images from unlabeled wild environments. Acknowledging
this we propose a novel appearance consensus driven self-supervised objective.
To effectively disentangle the foreground (FG) human we rely on image pairs
depicting the same person (consistent FG) in varied pose and background (BG)
which are obtained from unlabeled wild videos. The proposed FG appearance
consistency objective makes use of a novel, differentiable Color-recovery
module to obtain vertex colors without the need for any appearance network; via
efficient realization of color-picking and reflectional symmetry. We achieve
state-of-the-art results on the standard model-based 3D pose estimation
benchmarks at comparable supervision levels. Furthermore, the resulting colored
mesh prediction opens up the usage of our framework for a variety of
appearance-related tasks beyond the pose and shape estimation, thus
establishing our superior generalizability.
