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.

翻译：本文提出CaPhy方法，用于重建具有逼真动态服装特性的人体可动画化身。具体而言，我们致力于从真实观测中捕捉服装的几何与物理特性，使得化身在驱动新姿态时能产生符合物理规律的服装形变与褶皱。为此，我们结合基于物理损失的无监督训练与基于扫描数据的三维监督训练，构建兼具物理真实性且符合人体扫描数据的服装动态模型。通过引入物理损失梯度约束，我们还从扫描数据中优化底层物理模型的物理参数。与以往三维化身重建工作相比，本方法能泛化至新姿态并生成逼真的动态服装形变。多组实验表明，我们的方法可有效估计服装物理属性，在定量与定性指标上均优于现有方法。