CVPR2025
Quaffure: Real-Time Quasi-Static Neural Hair Simulation
Tuur Stuyck, Gene Wei-Chin Lin, Egor Larionov, Hsiao-Yu Chen, Aljaz Bozic, Nikolaos Sarafianos, Doug Roble
摘要
Meta Reality Labs * equal contributions Figure 1 . We present Quaffure, a real-time quasi-static neural hair simulator, which produces naturally draped hair in only a few milliseconds on commodity hardware, taking the hairstyle, body shape and pose into account. Our method scales to predicting the drape of 1000 hair grooms in just 0.3 seconds. Quaffure is trained using a physics-based self-supervised loss, eliminating the need for simulated training data that is costly and cumbersome to obtain. We show that our method works for a wide variety of body shapes and poses with a range of hairstyles varying from straight to curly, short to long.