TY  - JOUR
AU  - Huang, Hongxiang
AU  - An, Guoyuan
AU  - Lan, Jingzhen
AU  - Wang, Qi
AU  - Wang, Lingfei
AU  - Wang, Rui
AU  - Huo, Yuchi
PY  - 2025
TI  - Ultra-high resolution facial texture reconstruction from a single image
JO  - Computational Visual Media
SN  - 2096-0433
SP  - 781
EP  - 797
VL  - 11
IS  - 4
AB  - Advances in mobile cameras have made it easier to capture ultra-high resolution (UHR) portraits. However, existing face reconstruction methods lack specific adaptations for UHR input (e.g., 4096 × 4096), leading to under-use of high-frequency details that are crucial for achieving photorealistic rendering. Our method supports 4096×4096 UHR input and utilizes a divide-and-conquer approach for end-to-end 4K albedo, micronormal, and specular texture reconstruction at the original resolution. We employ a two-stage strategy to capture both global distributions and local high-frequency details, effectively mitigating mosaic and seam artifacts common in patch-based prediction. Additionally, we innovatively apply hash encoding to facial U-V coordinates to boost the model’s ability to learn regional high-frequency feature distributions. Our method can be easily incorporated in stateof-the-art facial geometry reconstruction pipelines, significantly improving the texture reconstruction quality, facilitating artistic creation workflows.
UR  - https://doi.org/10.26599/CVM.2025.9450488
DO  - 10.26599/CVM.2025.9450488