Frequency-Divided Learning of Fine-Grained Clothing Behavior via Flexible Dynamic Graphs.

Abstract

Despite significant advancements in neural simulation techniques for clothing animation, these methods struggle to capture the dynamic details of garments during movement. This limitation restricts their applicability in scenarios where high-quality garment deformation is essential. To address this challenge, we introduce a novel graph learning-based approach to enhance deformation realism through designed mechanisms for mesh information propagation and external optimization strategies during model training. First, we address the issue of over-smoothing common in conventional graph processing techniques by introducing a flexible message-passing method. This approach effectively manages node interactions within the mesh, thereby improving the expressiveness of the model. Furthermore, acknowledging that uniform model supervision typically neglects high-frequency details during optimization, we analyze the spectral properties of clothing meshes. Based on this analysis, we introduce a frequency-division constraint aligned with the characteristics of different frequency bands, which aids in precisely controlling the generation of details. Our model further integrates self-collision and other physics-aware losses, enabling the learning of generalized and fine-grained dynamic deformations. Extensive evaluations and comparisons demonstrate the effectiveness of our approach, showing notable improvements over existing state-of-the-art solutions.