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Neural Re-rendering of Humans from a Single Image

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Human re-rendering from a single image is a starkly underconstrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealistic distortions of the body parts and garments, or implausible changes of the texture. To address these challenges, we propose a new method for neural re-rendering of a human under a novel user-defined pose and viewpoint, given one input image. Our algorithm represents body pose and shape as a parametric mesh which can be reconstructed from a single image and easily reposed. Instead of a colour-based UV texture map, our approach further employs a learned high-dimensional UV feature map to encode appearance. This rich implicit representation captures detailed appearance variation across poses, viewpoints, person identities and clothing styles better than learned colour texture maps. The body model with the rendered feature maps is fed through a neural image-translation network that creates the final rendered colour image. The above components are combined in an end-to-end-trained neural network architecture that takes as input a source person image, and images of the parametric body model in the source pose and desired target pose. Experimental evaluation demonstrates that our approach produces higher quality single-image re-rendering results than existing methods.

Project webpage: http://gvv.mpi-inf.mpg.de/projects/NHRR/.

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Acknowledgements

This work was supported by the ERC Consolidator Grant 4DReply (770784).

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Authors and Affiliations

  1. MPI for Informatics, SIC, Saarbrücken, Germany

    Kripasindhu Sarkar, Dushyant Mehta, Vladislav Golyanik & Christian Theobalt

  2. Facebook Reality Labs, Pittsburgh, USA

    Weipeng Xu

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  1. Kripasindhu Sarkar
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  2. Dushyant Mehta
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  3. Weipeng Xu
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  4. Vladislav Golyanik
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  5. Christian Theobalt
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Correspondence to Kripasindhu Sarkar .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Sarkar, K., Mehta, D., Xu, W., Golyanik, V., Theobalt, C. (2020). Neural Re-rendering of Humans from a Single Image. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12356. Springer, Cham. https://doi.org/10.1007/978-3-030-58621-8_35

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