Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
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Abstract (arXiv)
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.
Key facts. Cited by 126 papers in the map (data through 14 Oct 2026). Builds on 38 of its 48 references that are in the map. First posted on arXiv 19 Apr 2022.
Builds on
38 of 48 references.
References · 38
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- 2020Implicit Functions in Feature Space for 3D Shape Reconstruction and Completioncited by 343
- 2020ARCH: Animatable Reconstruction of Clothed Humanscited by 257
- 2019Multi-Garment Net: Learning to Dress 3D People from Imagescited by 245
- 2018Video Based Reconstruction of 3D People Modelscited by 238
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- 2017Photographic Image Synthesis with Cascaded Refinement Networkscited by 223
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- 2021Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Controlcited by 106
- 2021imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Posecited by 103
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Citing papers per year: 2022: 15; 2023: 44; 2024: 34; 2025: 26; 2026: 7.
126 papers.
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50 most cited of 126 All 126 (JSON)
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