  This paper presents a novel learning-based clothing deformation method to
generate rich and reasonable detailed deformations for garments worn by bodies
of various shapes in various animations. In contrast to existing learning-based
methods, which require numerous trained models for different garment topologies
or poses and are unable to easily realize rich details, we use a unified
framework to produce high fidelity deformations efficiently and easily. To
address the challenging issue of predicting deformations influenced by
multi-source attributes, we propose three strategies from novel perspectives.
Specifically, we first found that the fit between the garment and the body has
an important impact on the degree of folds. We then designed an attribute
parser to generate detail-aware encodings and infused them into the graph
neural network, therefore enhancing the discrimination of details under diverse
attributes. Furthermore, to achieve better convergence and avoid overly smooth
deformations, we proposed output reconstruction to mitigate the complexity of
the learning task. Experiment results show that our proposed deformation method
achieves better performance over existing methods in terms of generalization
ability and quality of details.
