用神经网络实时模拟逼真发型，支持多种姿态与发式。
Quaffure: Real-Time Quasi-Static Neural Hair Simulation
- 自监督训练，无需昂贵数据生成与存储。
- 推理仅需几毫秒，0.3秒可处理1000个角色的发型下垂。
- 适合游戏、虚拟形象等实时应用，泛化能力强。
真实感发型运动对高质量虚拟形象至关重要，但常受限于实时应用的计算资源。为此，我们提出一种新型神经方法，预测物理上合理的发型形变，并能泛化至不同身体姿态、体型和发型。模型采用自监督损失训练，无需昂贵的数据生成与存储。通过大量实验在多种姿态与体型变化下验证了方法的有效性，展示了其强大的泛化能力与时间上平滑的结果。该方法在消费级硬件上推理时间仅数毫秒，且可扩展至0.3秒内完成1000名新郎发型下垂的预测，非常适合实时应用场景。
原文摘要 · Abstract (English)
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. Please see our project page here following https://tuurstuyck.github.io/quaffure/quaffure.html
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