@article{Huang2025, 
author = {Hongxiang Huang and Guoyuan An and Jingzhen Lan and Qi Wang and Lingfei Wang and Rui Wang and Yuchi Huo},
title = {Ultra-high resolution facial texture reconstruction from a single image},
year = {2025},
journal = {Computational Visual Media},
volume = {11},
number = {4},
pages = {781-797},
keywords = {face reconstruction, texture reconstruction, ultra-high resolution, single image synthesis},
url = {https://www.sciopen.com/article/10.26599/CVM.2025.9450488},
doi = {10.26599/CVM.2025.9450488},
abstract = {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.}
}