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.

该研究论文介绍了一种称为 CLOAF 的方法，通过利用普通微分方程的可微性来消除身体自相交问题，同时保持身体形状约束，并展示了该方法相比之前的方法可以完全消除自相交，而不影响重建的准确性。CLOAF 还能用于优化姿势和形状估计基线，提高其整体性能并消除预测中的自相交问题。此外，研究还展示了如何将 CLOAF 策略应用于用户造成的任何运动场景，并实现通过编辑运动与环境进行交互而不用担心潜在碰撞或身体形状的丢失。