CGHair: Compact Gaussian Hair Reconstruction with Card Clustering
For computer graphics and VR/AR applications, this enables efficient hair modeling from multi-view images with drastically reduced memory and time costs.
CGHair reduces hair reconstruction storage by over 200x and speeds up strand reconstruction by 4x while maintaining visual quality comparable to 3DGS methods.
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.