CGHair: Compact Gaussian Hair Reconstruction with Card Clustering
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 geo...
| Authors | Haimin Luo, Srinjay Sarkar, Albert Mosella-Montoro et al. |
|---|---|
| Primary category | cs.CV |
| Published | 4 April 2026 |
| Last updated at source | 4 April 2026 |
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