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Super-Resolution 3D Human Shape from a Single Low-Resolution Image

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13662))

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Abstract

We propose a novel framework to reconstruct super-resolution human shape from a single low-resolution input image. The approach overcomes limitations of existing approaches that reconstruct 3D human shape from a single image, which require high-resolution images together with auxiliary data such as surface normal or a parametric model to reconstruct high-detail shape. The proposed framework represents the reconstructed shape with a high-detail implicit function. Analogous to the objective of 2D image super-resolution, the approach learns the mapping from a low-resolution shape to its high-resolution counterpart and it is applied to reconstruct 3D shape detail from low-resolution images. The approach is trained end-to-end employing a novel loss function which estimates the information lost between a low and high-resolution representation of the same 3D surface shape. Evaluation for single image reconstruction of clothed people demonstrates that our method achieves high-detail surface reconstruction from low-resolution images without auxiliary data. Extensive experiments show that the proposed approach can estimate super-resolution human geometries with a significantly higher level of detail than that obtained with previous approaches when applied to low-resolution images. https://marcopesavento.github.io/SuRS/.

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Acknowledgement

This research was supported by UKRI EPSRC Platform Grant EP/P022529/1.

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Authors and Affiliations

  1. Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey, Guildford, UK

    Marco Pesavento, Marco Volino & Adrian Hilton

Authors
  1. Marco Pesavento
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  2. Marco Volino
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  3. Adrian Hilton
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Corresponding author

Correspondence to Marco Pesavento .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Pesavento, M., Volino, M., Hilton, A. (2022). Super-Resolution 3D Human Shape from a Single Low-Resolution Image. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13662. Springer, Cham. https://doi.org/10.1007/978-3-031-20086-1_26

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  • DOI: https://doi.org/10.1007/978-3-031-20086-1_26

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