LFGarNet: Loose-Fitting Garment Animation With Multi-Attribute-Aware Graph Network

Current AI animation generation methods excel in tight-fitting clothing scenarios but struggle with deformation distortion and the gradual loss of wrinkles over extended simulations in loose-fitting clothing. To address these issues, we propose a multi-attribute-aware Graph Network. This approach mitigates the gradual loss of wrinkles by dividing animation sequences into multiple stages based on motion categories, recognizing that identical body postures can cause different clothing deformations due to varying motion tendencies. In each stage, we first restore coarse, globally guided deformations based on the motion category, followed by enhancing detailed features. We observed that garments within the same sport category exhibit similar local wrinkles and that the degree of fit to the body varies significantly across different regions of the same garment. We introduce two specific clothing attributes: “looseness” and “deformity,” which relate to local wrinkles and have physical significance. A clothing attribute encoder perceives these attributes and constructs a clothing graph model to estimate detailed features. Our method effectively handles clothing deformations across various motion types, including extreme postures, with qualitative and quantitative analyses confirming its effectiveness.