CLOAF: CoLlisiOn-Aware Human Flow
Andrey Davydov Martin Engilberge Mathieu Salzmann Pascal Fua
Even the best current algorithms for estimating body 3D shape and pose yield
results that include body self-intersections. In this paper, we present CLOAF,
which exploits the diffeomorphic nature of Ordinary Differential Equations to
eliminate such self-intersections while still imposing body shape constraints.
We show that, unlike earlier approaches to addressing this issue, ours
completely eliminates the self-intersections without compromising the accuracy
of the reconstructions. Being differentiable, CLOAF can be used to fine-tune
pose and shape estimation baselines to improve their overall performance and
eliminate self-intersections in their predictions. Furthermore, we demonstrate
how our CLOAF strategy can be applied to practically any motion field induced
by the user. CLOAF also makes it possible to edit motion to interact with the
environment without worrying about potential collision or loss of body-shape
prior.