Achieving realistic hair strand synthesis is essential for creating lifelike digital humans, but producing high-fidelity hair strand geometry remains a significant challenge. Existing methods require a complex setup for data acquisition, involving multi-view images captured in constrained studio environments. Additionally, these methods have longer hair volume estimation and strand synthesis times, which hinder efficiency. We introduce PanoHair, a model that estimates head geometry as signed distance fields using knowledge distillation from a pre-trained generative teacher model for head synthesis. Our approach enables the prediction of semantic segmentation masks and 3D orientations specifically for the hair region of the estimated geometry. Our method is generative and can generate diverse hairstyles with latent space manipulations. For real images, our approach involves an inversion process to infer latent codes and produces visually appealing hair strands, offering a streamlined alternative to complex multi-view data acquisition setups. Given the latent code, PanoHair generates a clean manifold mesh for the hair region in under 5 seconds, along with semantic and orientation maps, marking a significant improvement over existing methods, as demonstrated in our experiments.

翻译：实现逼真的发丝合成对于创建栩栩如生的数字人至关重要，但生成高保真度的发丝几何结构仍然是一项重大挑战。现有方法需要复杂的数据采集设置，涉及在受限的摄影棚环境中捕获多视角图像。此外，这些方法的头发体积估计和发丝合成时间较长，影响了效率。我们提出了PanoHair模型，该模型通过从预训练的头部合成生成式教师模型中提取知识，将头部几何结构估计为有符号距离场。我们的方法能够专门针对估计几何结构的头发区域预测语义分割掩码和三维方向场。本方法是生成式的，可通过潜在空间操作生成多样化的发型。对于真实图像，我们的方法包含一个推断潜在码的反演过程，并生成视觉上吸引人的发丝，为复杂的多视角数据采集设置提供了简化的替代方案。给定潜在码后，PanoHair可在5秒内为头发区域生成干净的流形网格，同时输出语义图和方向图，这标志着相对于现有方法的显著改进，正如我们的实验所证明的。