  We propose RANA, a relightable and articulated neural avatar for the
photorealistic synthesis of humans under arbitrary viewpoints, body poses, and
lighting. We only require a short video clip of the person to create the avatar
and assume no knowledge about the lighting environment. We present a novel
framework to model humans while disentangling their geometry, texture, and also
lighting environment from monocular RGB videos. To simplify this otherwise
ill-posed task we first estimate the coarse geometry and texture of the person
via SMPL+D model fitting and then learn an articulated neural representation
for photorealistic image generation. RANA first generates the normal and albedo
maps of the person in any given target body pose and then uses spherical
harmonics lighting to generate the shaded image in the target lighting
environment. We also propose to pretrain RANA using synthetic images and
demonstrate that it leads to better disentanglement between geometry and
texture while also improving robustness to novel body poses. Finally, we also
present a new photorealistic synthetic dataset, Relighting Humans, to
quantitatively evaluate the performance of the proposed approach.
