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Photo-realistic re-rendering of a human from a single image with explicit control over body pose, shape and appearance enables a wide range of applications, such as human appearance transfer, virtual try-on, motion imitation, and novel view synthesis.
Image quality assessment: from error visibility to structural similarity
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Deep Video Portraits
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cGANs with Projection Discriminator. In International Conference on Learning Representations (ICLR)
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Natalia Neverova, Riza Alp Güler, and Iasonas Kokkinos. 2018 · 2018
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Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
Lingjie Liu, Weipeng Xu, Marc Habermann, Michael Zollhöfer, Florian Bernard, Hyeongwoo Kim, Wenping Wang, and Christian Theobalt. 2020b · 2020
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D-NeRF: Neural Radiance Fields for Dynamic Scenes
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PVA: Pixel-aligned Volumetric Avatars. In arXiv:2101.02697
Amit Raj, Michael Zollhoefer, Tomas Simon, Jason Saragih, Shunsuke Saito, James Hays, and Stephen Lombardi. 2020 · 2020
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Neural Re-Rendering of Humans from a Single Image. In European Conference on Computer Vision (ECCV)
Kripasindhu Sarkar, Dushyant Mehta, Weipeng Xu, Vladislav Golyanik, and Christian Theobalt. 2020 · 2020
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PIE: Portrait Image Embedding for Semantic Control
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NERF++: Analyzing and Improving Neural Radiance Fields
Kai Zhang, Gernot Riegler, Noah Snavely, and Vladlen Koltun. 2020 · 2020
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VOGUE: Try-On by StyleGAN Interpolation Optimization
Kathleen M Lewis, Srivatsan Varadharajan, and Ira Kemelmacher-Shlizerman. 2021 · 2021
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GAN-Control: Explicitly Controllable GANs
Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, and Gerard Medioni. 2021 · 2021
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