Quaffure: Real-Time Quasi-Static Neural Hair Simulation

• URL: http://arxiv.org/abs/2412.10061v1
• Date: Fri, 13 Dec 2024 11:44:56 GMT
• Title: Quaffure: Real-Time Quasi-Static Neural Hair Simulation
• Authors: Tuur Stuyck, Gene Wei-Chin Lin, Egor Larionov, Hsiao-yu Chen, Aljaz Bozic, Nikolaos Sarafianos, Doug Roble,
• Abstract summary: We propose a novel neural approach to predict hair deformations that generalizes to various body poses, shapes, and hairstyles. Our model is trained using a self-supervised loss, eliminating the need for expensive data generation and storage. Our approach is highly suitable for real-time applications with an inference time of only a few milliseconds on consumer hardware.
• Score: 11.869362129320473
• License:
• Abstract: Realistic hair motion is crucial for high-quality avatars, but it is often limited by the computational resources available for real-time applications. To address this challenge, we propose a novel neural approach to predict physically plausible hair deformations that generalizes to various body poses, shapes, and hairstyles. Our model is trained using a self-supervised loss, eliminating the need for expensive data generation and storage. We demonstrate our method's effectiveness through numerous results across a wide range of pose and shape variations, showcasing its robust generalization capabilities and temporally smooth results. Our approach is highly suitable for real-time applications with an inference time of only a few milliseconds on consumer hardware and its ability to scale to predicting the drape of 1000 grooms in 0.3 seconds.

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