We present CaPhy, a novel method for reconstructing animatable human avatars with realistic dynamic properties for clothing. Specifically, we aim for capturing the geometric and physical properties of the clothing from real observations. This allows us to apply novel poses to the human avatar with physically correct deformations and wrinkles of the clothing. To this end, we combine unsupervised training with physics-based losses and 3D-supervised training using scanned data to reconstruct a dynamic model of clothing that is physically realistic and conforms to the human scans. We also optimize the physical parameters of the underlying physical model from the scans by introducing gradient constraints of the physics-based losses. In contrast to previous work on 3D avatar reconstruction, our method is able to generalize to novel poses with realistic dynamic cloth deformations. Experiments on several subjects demonstrate that our method can estimate the physical properties of the garments, resulting in superior quantitative and qualitative results compared with previous methods.

我们提出了CaPhy，一种用于重建具有逼真动态属性的可动画人体角色的新方法，重点是捕捉衣物的几何和物理属性以及在真实观察中应用新颖的动作，以物理正确的变形和皱纹覆盖人体角色，通过与物理损失的非监督训练以及使用扫描数据的三维监督训练结合的方式重建具有物理逼真性和符合人体扫描的衣物的动态模型，同时通过引入物理损失的梯度约束来优化扫描数据下潜在物理模型的物理参数，与之前的3D角色重建工作相比，我们的方法能够泛化到具有逼真的动态布料变形的新动作，对多个主题进行的实验表明我们的方法能够估计服装的物理属性，从而得到优于之前方法的定量和定性结果。

CaPhy: 捕捉用于可动人体角色的物理性质