  Garment representation, editing and animation are challenging topics in the
area of computer vision and graphics. It remains difficult for existing garment
representations to achieve smooth and plausible transitions between different
shapes and topologies. In this work, we introduce, DeepCloth, a unified
framework for garment representation, reconstruction, animation and editing.
Our unified framework contains 3 components: First, we represent the garment
geometry with a "topology-aware UV-position map", which allows for the unified
description of various garments with different shapes and topologies by
introducing an additional topology-aware UV-mask for the UV-position map.
Second, to further enable garment reconstruction and editing, we contribute a
method to embed the UV-based representations into a continuous feature space,
which enables garment shape reconstruction and editing by optimization and
control in the latent space, respectively. Finally, we propose a garment
animation method by unifying our neural garment representation with body shape
and pose, which achieves plausible garment animation results leveraging the
dynamic information encoded by our shape and style representation, even under
drastic garment editing operations. To conclude, with DeepCloth, we move a step
forward in establishing a more flexible and general 3D garment digitization
framework. Experiments demonstrate that our method can achieve state-of-the-art
garment representation performance compared with previous methods.
