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.

本文提出了一种基于数据驱动的方法，利用预计算辐射传输和球谐光对全身人物图像进行自动换光。与以往工作不同的是，我们通过显式建模散射和镜面反射来消除对Lambertian材料的假设，同时引入了基于光的残差项来补偿PRT图像重建中的误差。我们提出了一种新的深度学习架构，经过特殊的PRT分解训练，并使用L1、对数和渲染损失的组合进行训练。对于合成图像和照片，我们的模型在全身人类换光方面均优于现有技术。

单张图像全身人物再照明