  We present PHORHUM, a novel, end-to-end trainable, deep neural network
methodology for photorealistic 3D human reconstruction given just a monocular
RGB image. Our pixel-aligned method estimates detailed 3D geometry and, for the
first time, the unshaded surface color together with the scene illumination.
Observing that 3D supervision alone is not sufficient for high fidelity color
reconstruction, we introduce patch-based rendering losses that enable reliable
color reconstruction on visible parts of the human, and detailed and plausible
color estimation for the non-visible parts. Moreover, our method specifically
addresses methodological and practical limitations of prior work in terms of
representing geometry, albedo, and illumination effects, in an end-to-end model
where factors can be effectively disentangled. In extensive experiments, we
demonstrate the versatility and robustness of our approach. Our
state-of-the-art results validate the method qualitatively and for different
metrics, for both geometric and color reconstruction.
