  We consider the problem of obese human mesh recovery, i.e., fitting a
parametric human mesh to images of obese people. Despite obese person mesh
fitting being an important problem with numerous applications (e.g.,
healthcare), much recent progress in mesh recovery has been restricted to
images of non-obese people. In this work, we identify this crucial gap in the
current literature by presenting and discussing limitations of existing
algorithms. Next, we present a simple baseline to address this problem that is
scalable and can be easily used in conjunction with existing algorithms to
improve their performance. Finally, we present a generalized human mesh
optimization algorithm that substantially improves the performance of existing
methods on both obese person images as well as community-standard benchmark
datasets. A key innovation of this technique is that it does not rely on
supervision from expensive-to-create mesh parameters. Instead, starting from
widely and cheaply available 2D keypoints annotations, our method automatically
generates mesh parameters that can in turn be used to re-train and fine-tune
any existing mesh estimation algorithm. This way, we show our method acts as a
drop-in to improve the performance of a wide variety of contemporary mesh
estimation methods. We conduct extensive experiments on multiple datasets
comprising both standard and obese person images and demonstrate the efficacy
of our proposed techniques.
