  We consider the problem of estimating frame-level full human body meshes
given a video of a person with natural motion dynamics. While much progress in
this field has been in single image-based mesh estimation, there has been a
recent uptick in efforts to infer mesh dynamics from video given its role in
alleviating issues such as depth ambiguity and occlusions. However, a key
limitation of existing work is the assumption that all the observed motion
dynamics can be modeled using one dynamical/recurrent model. While this may
work well in cases with relatively simplistic dynamics, inference with
in-the-wild videos presents many challenges. In particular, it is typically the
case that different body parts of a person undergo different dynamics in the
video, e.g., legs may move in a way that may be dynamically different from
hands (e.g., a person dancing). To address these issues, we present a new
method for video mesh recovery that divides the human mesh into several local
parts following the standard skeletal model. We then model the dynamics of each
local part with separate recurrent models, with each model conditioned
appropriately based on the known kinematic structure of the human body. This
results in a structure-informed local recurrent learning architecture that can
be trained in an end-to-end fashion with available annotations. We conduct a
variety of experiments on standard video mesh recovery benchmark datasets such
as Human3.6M, MPI-INF-3DHP, and 3DPW, demonstrating the efficacy of our design
of modeling local dynamics as well as establishing state-of-the-art results
based on standard evaluation metrics.
