用Transformer实现跨发型、身形和动作的逼真头发动态模拟
HairFormer: Transformer-Based Dynamic Neural Hair Simulation
- 分两阶段：先用Transformer预测静态披覆形状，再融合运动信息生成动态
- 支持实时推理，可处理长发等复杂未见发型的穿透问题
- 适合影视动画中需要快速调整复杂发型动态的场景
模拟能泛化到任意发型、身体形态和动作的头发动态是一项关键挑战。我们提出一种基于Transformer的两阶段神经方法，首次实现了此类广泛泛化。首先，通过基于Transformer的静态网络预测任意发型的静态披覆形状，有效解决头发与身体穿插问题并保持头发保真度；随后，采用带有新型交叉注意力机制的动态网络，将静态头发特征与运动输入融合，生成富有表现力的动态及复杂的二次运动。该动态网络还支持对如突发头部动作等复杂运动序列的高效微调。我们的方法可在单帧静态披覆和姿态序列上的动态披覆实现实时推理。实验表明，该方法在多种发型下均表现出高保真度和强泛化能力，借助物理感知损失，即使面对复杂且未见过的长发也能解决穿透问题，凸显其广泛的适用性。
原文摘要 · Abstract (English)
Simulating hair dynamics that generalize across arbitrary hairstyles, body shapes, and motions is a critical challenge. Our novel two-stage neural solution is the first to leverage Transformer-based architectures for such a broad generalization. We propose a Transformer-powered static network that predicts static draped shapes for any hairstyle, effectively resolving hair-body penetrations and preserving hair fidelity. Subsequently, a dynamic network with a novel cross-attention mechanism fuses static hair features with kinematic input to generate expressive dynamics and complex secondary motions. This dynamic network also allows for efficient fine-tuning of challenging motion sequences, such as abrupt head movements. Our method offers real-time inference for both static single-frame drapes and dynamic drapes over pose sequences. Our method demonstrates high-fidelity and generalizable dynamic hair across various styles, guided by physics-informed losses, and can resolve penetrations even for complex, unseen long hairstyles, highlighting its broad generalization.
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