  Existing neural human rendering methods struggle with a single image input
due to the lack of information in invisible areas and the depth ambiguity of
pixels in visible areas. In this regard, we propose Monocular Neural Human
Renderer (MonoNHR), a novel approach that renders robust free-viewpoint images
of an arbitrary human given only a single image. MonoNHR is the first method
that (i) renders human subjects never seen during training in a monocular
setup, and (ii) is trained in a weakly-supervised manner without geometry
supervision. First, we propose to disentangle 3D geometry and texture features
and to condition the texture inference on the 3D geometry features. Second, we
introduce a Mesh Inpainter module that inpaints the occluded parts exploiting
human structural priors such as symmetry. Experiments on ZJU-MoCap, AIST, and
HUMBI datasets show that our approach significantly outperforms the recent
methods adapted to the monocular case.
