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We present PHORHUM, a novel, end-to-end trainable, deep neural networkmethodology for photorealistic 3D human reconstruction given just a monocularRGB image. Our pixel-aligned method estimates detailed 3D geometry and, for thefirst time, the unshaded surface color together with the scene illumination.Observing that 3D supervision alone is not sufficient for high fidelity colorreconstruction, we introduce patch-based rendering losses that enable reliablecolor reconstruction on visible parts of the human, and detailed and plausiblecolor estimation for the non-visible parts. Moreover, our method specificallyaddresses methodological and practical limitations of prior work in terms ofrepresenting geometry, albedo, and illumination effects, in an end-to-end modelwhere factors can be effectively disentangled. In extensive experiments, wedemonstrate the versatility and robustness of our approach. Ourstate-of-the-art results validate the method qualitatively and for differentmetrics, for both geometric and color reconstruction.

Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing