Existing data-driven methods for draping garments over human bodies, despite
being effective, cannot handle garments of arbitrary topology and are typically
not end-to-end differentiable. To address these limitations, we propose an
end-to-end differentiable pipeline that represents garments using implicit
surfaces and learns a skinning field conditioned on shape and pose parameters
of an articulated body model. To limit body-garment interpenetrations and
artifacts, we propose an interpenetration-aware pre-processing strategy of
training data and a novel training loss that penalizes self-intersections while
draping garments. We demonstrate that our method yields more accurate results
for garment reconstruction and deformation with respect to state of the art
methods. Furthermore, we show that our method, thanks to its end-to-end
differentiability, allows to recover body and garments parameters jointly from
image observations, something that previous work could not do.

我们提出了一种使用隐式曲面表示衣服、学习受关节体模型形状和姿势参数影响的蒙皮场的端到端可微管道来处理任意拓扑衣物的方法，并提出了一种防止体 - 衣物相互穿插和减少艺术品的预处理策略和新的训练损失。经实验证明，我们的方法相比于现有的方法，可以更准确地重建和变形服装，并且可以通过图像观测共同恢复身体和服装参数。