We present a single-image data-driven method to automatically relight images with full-body humans in them. Our framework is based on a realistic scene decomposition leveraging precomputed radiance transfer (PRT) and spherical harmonics (SH) lighting. In contrast to previous work, we lift the assumptions on Lambertian materials and explicitly model diffuse and specular reflectance in our data. Moreover, we introduce an additional light-dependent residual term that accounts for errors in the PRT-based image reconstruction. We propose a new deep learning architecture, tailored to the decomposition performed in PRT, that is trained using a combination of L1, logarithmic, and rendering losses. Our model outperforms the state of the art for full-body human relighting both with synthetic images and photographs.

中文翻译：

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单图像全身人体重新照明

我们提出了一种单图像数据驱动的方法来自动重新点亮包含全身人体的图像。我们的框架基于现实场景分解，利用预先计算的辐射传递 (PRT) 和球谐函数 (SH) 照明。与之前的工作相比，我们取消了对朗伯材料的假设，并在我们的数据中明确模拟漫反射和镜面反射。此外，我们引入了一个额外的光相关残差项，用于解释基于 PRT 的图像重建中的错误。我们提出了一种新的深度学习架构，专为 PRT 中执行的分解量身定制，使用 L1、对数和渲染损失的组合进行训练。我们的模型在合成图像和照片方面都优于最先进的全身人体重新照明。