  Our work presents a novel spectrum-inspired learning-based approach for
generating clothing deformations with dynamic effects and personalized details.
Existing methods in the field of clothing animation are limited to either
static behavior or specific network models for individual garments, which
hinders their applicability in real-world scenarios where diverse animated
garments are required. Our proposed method overcomes these limitations by
providing a unified framework that predicts dynamic behavior for different
garments with arbitrary topology and looseness, resulting in versatile and
realistic deformations. First, we observe that the problem of bias towards low
frequency always hampers supervised learning and leads to overly smooth
deformations. To address this issue, we introduce a frequency-control strategy
from a spectral perspective that enhances the generation of high-frequency
details of the deformation. In addition, to make the network highly
generalizable and able to learn various clothing deformations effectively, we
propose a spectral descriptor to achieve a generalized description of the
global shape information. Building on the above strategies, we develop a
dynamic clothing deformation estimator that integrates frequency-controllable
attention mechanisms with long short-term memory. The estimator takes as input
expressive features from garments and human bodies, allowing it to
automatically output continuous deformations for diverse clothing types,
independent of mesh topology or vertex count. Finally, we present a neural
collision handling method to further enhance the realism of garments. Our
experimental results demonstrate the effectiveness of our approach on a variety
of free-swinging garments and its superiority over state-of-the-art methods.
