CaPhy: Capturing Physical Properties for Animatable Human Avatars
This addresses the challenge of generating physically correct clothing deformations for human avatars in novel poses, which is incremental over prior 3D avatar reconstruction work.
The paper tackles the problem of reconstructing animatable human avatars with realistic clothing dynamics by capturing geometric and physical properties from real observations, resulting in superior quantitative and qualitative results compared to previous methods.
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.