  This paper considers a new problem of adapting a pre-trained model of human
mesh reconstruction to out-of-domain streaming videos. However, most previous
methods based on the parametric SMPL model \cite{loper2015smpl} underperform in
new domains with unexpected, domain-specific attributes, such as camera
parameters, lengths of bones, backgrounds, and occlusions. Our general idea is
to dynamically fine-tune the source model on test video streams with additional
temporal constraints, such that it can mitigate the domain gaps without
over-fitting the 2D information of individual test frames. A subsequent
challenge is how to avoid conflicts between the 2D and temporal constraints. We
propose to tackle this problem using a new training algorithm named Bilevel
Online Adaptation (BOA), which divides the optimization process of overall
multi-objective into two steps of weight probe and weight update in a training
iteration. We demonstrate that BOA leads to state-of-the-art results on two
human mesh reconstruction benchmarks.
