  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.
