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-...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.展开更多
基金supported by the National Key R&D Program of China(2024YDLN0011)the Key R&D Program of Zhejiang Province(2023C01039).
文摘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.
