  Single-image human relighting aims to relight a target human under new
lighting conditions by decomposing the input image into albedo, shape and
lighting. Although plausible relighting results can be achieved, previous
methods suffer from both the entanglement between albedo and lighting and the
lack of hard shadows, which significantly decrease the realism. To tackle these
two problems, we propose a geometry-aware single-image human relighting
framework that leverages single-image geometry reconstruction for joint
deployment of traditional graphics rendering and neural rendering techniques.
For the de-lighting, we explore the shortcomings of UNet architecture and
propose a modified HRNet, achieving better disentanglement between albedo and
lighting. For the relighting, we introduce a ray tracing-based per-pixel
lighting representation that explicitly models high-frequency shadows and
propose a learning-based shading refinement module to restore realistic shadows
(including hard cast shadows) from the ray-traced shading maps. Our framework
is able to generate photo-realistic high-frequency shadows such as cast shadows
under challenging lighting conditions. Extensive experiments demonstrate that
our proposed method outperforms previous methods on both synthetic and real
images.
