  Rendering 3D human appearance in different views is crucial for achieving
holographic communication and immersive VR/AR. Existing methods either rely on
multi-camera setups or have low-quality rendered images from a single image. In
this paper, we propose R2Human, the first approach for real-time inference and
rendering of photorealistic 3D human appearance from a single image. The core
of our approach is to combine the strengths of implicit texture fields and
explicit neural rendering with our novel representation, namely Z-map. Based on
this, we present an end-to-end network that performs high-fidelity color
reconstruction of visible areas and provides reliable color inference for
occluded regions. To further enhance the 3D perception ability of our network,
we leverage the Fourier occupancy field as a prior for generating the texture
field and providing a sampling surface in the rendering stage. We also propose
a consistency loss and a spatio-temporal fusion strategy to ensure the
multi-view coherence. Experimental results show that our method outperforms the
state-of-the-art methods on both synthetic data and challenging real-world
images, in real time.
