We present a compact pipeline for high-fidelity hair reconstruction from multi-view images. While recent 3D Gaussian Splatting (3DGS) methods achieve realistic results, they often require millions of primitives, leading to high storage and rendering costs. Observing that hair exhibits structural and visual similarities across a hairstyle, we cluster strands into representative hair cards and group these into shared texture codebooks. Our approach integrates this structure with 3DGS rendering, significantly reducing reconstruction time and storage while maintaining comparable visual quality. In addition, we propose a generative prior accelerated method to reconstruct the initial strand geometry from a set of images. Our experiments demonstrate a 4-fold reduction in strand reconstruction time and achieve comparable rendering performance with over 200x lower memory footprint.

翻译：我们提出了一种从多视角图像进行高保真头发重建的紧凑流水线。尽管最新的三维高斯溅射(3DGS)方法能够实现逼真的结果，但其通常需要数百万个图元，导致高昂的存储和渲染成本。鉴于头发在不同发型中存在结构性和视觉上的相似性，我们将发丝聚类为具有代表性的头发卡片，并将这些卡片分组至共享纹理编码本。我们的方法将该结构与3DGS渲染相结合，在保持相当视觉质量的同时，显著减少了重建时间和存储需求。此外，我们提出了一种生成先验加速方法，从一组图像中重建初始发丝几何结构。实验表明，发丝重建时间降低了4倍，且渲染性能相当，内存占用降低超过200倍。