CGHair: Compact Gaussian Hair Reconstruction with Card Clustering
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866908936854044672 |
|---|---|
| author | Luo, Haimin Sarkar, Srinjay Mosella-Montoro, Albert Carrasco, Francisco Vicente De la Torre, Fernando |
| author_facet | Luo, Haimin Sarkar, Srinjay Mosella-Montoro, Albert Carrasco, Francisco Vicente De la Torre, Fernando |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03716 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CGHair: Compact Gaussian Hair Reconstruction with Card Clustering Luo, Haimin Sarkar, Srinjay Mosella-Montoro, Albert Carrasco, Francisco Vicente De la Torre, Fernando Computer Vision and Pattern Recognition Graphics 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. |
| title | CGHair: Compact Gaussian Hair Reconstruction with Card Clustering |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2604.03716 |