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SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects

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Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12367))

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Abstract

Recent advances in image generation gave rise to powerful tools for semantic image editing. However, existing approaches can either operate on a single image or require an abundance of additional information. They are not capable of handling the complete set of editing operations, that is addition, manipulation or removal of semantic concepts. To address these limitations, we propose SESAME, a novel generator-discriminator pair for Semantic Editing of Scenes by Adding, Manipulating or Erasing objects. In our setup, the user provides the semantic labels of the areas to be edited and the generator synthesizes the corresponding pixels. In contrast to previous methods that employ a discriminator that trivially concatenates semantics and image as an input, the SESAME discriminator is composed of two input streams that independently process the image and its semantics, using the latter to manipulate the results of the former. We evaluate our model on a diverse set of datasets and report state-of-the-art performance on two tasks: (a) image manipulation and (b) image generation conditioned on semantic labels.

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Acknowledgements

This work was partly supported by CSEM, ETH Zurich Fund (OK) and by Huawei, Amazon AWS and Nvidia GPU grants. We are grateful to Despoina Paschalidou, Siavash Bigdeli and Danda Pani Paudel for fruitful discussions. We also thank Gene Kogan for providing guidance on how to prepare the Flickr Landscapes Dataset.

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Authors and Affiliations

  1. Computer Vision Lab, ETH Zurich, Zürich, Switzerland

    Evangelos Ntavelis, Andrés Romero, Luc Van Gool & Radu Timofte

  2. Robotics and Machine Learning, CSEM SA, Alpnach, Switzerland

    Evangelos Ntavelis & Iason Kastanis

  3. PSI, ESAT, KU Leuven, Leuven, Belgium

    Luc Van Gool

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  1. Evangelos Ntavelis
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  4. Luc Van Gool
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  5. Radu Timofte
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Correspondence to Evangelos Ntavelis .

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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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Ntavelis, E., Romero, A., Kastanis, I., Van Gool, L., Timofte, R. (2020). SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12367. Springer, Cham. https://doi.org/10.1007/978-3-030-58542-6_24

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