  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.
