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.

翻译：我们的工作提出了一种新颖的、受频谱启发的学习方法，用于生成具有动态效果和个性化细节的衣物变形。现有衣物动画领域的方法局限于静态行为或针对单件衣物的特定网络模型，这阻碍了它们在需要多样化动态衣物的真实场景中的应用。我们提出的方法通过提供一个统一框架克服了这些局限，该框架能够预测具有任意拓扑结构和松紧度的不同衣物的动态行为，从而产生多样且逼真的变形。首先，我们观察到低频偏置问题总是阻碍监督学习，导致变形过于平滑。为解决这一问题，我们从频谱角度引入一种频率控制策略，增强变形中高频细节的生成。此外，为使网络具有高度泛化能力并能有效学习各种衣物变形，我们提出一种频谱描述子，以实现对全局形状信息的通用描述。基于上述策略，我们开发了一个动态衣物变形估计器，将可频率控制的注意力机制与长短期记忆网络相结合。该估计器以衣物和人体的表达性特征为输入，能够自动为不同类型衣物输出连续变形，且独立于网格拓扑结构或顶点数量。最后，我们提出一种神经碰撞处理方法，以进一步提升衣物的真实感。实验结果表明，我们的方法在多种自由摆动衣物上的有效性，以及相较于现有最先进方法的优越性。