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Image-Based CLIP-Guided Essence Transfer

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We make the distinction between (i) style transfer, in which a source image is manipulated to match the textures and colors of a target image, and (ii) essence transfer, in which one edits the source image to include high-level semantic attributes from the target. Crucially, the semantic attributes that constitute the essence of an image may differ from image to image. Our blending operator combines the powerful StyleGAN generator and the semantic encoder of CLIP in a novel way that is simultaneously additive in both latent spaces, resulting in a mechanism that guarantees both identity preservation and high-level feature transfer without relying on a facial recognition network. We present two variants of our method. The first is based on optimization, while the second fine-tunes an existing inversion encoder to perform essence extraction. Through extensive experiments, we demonstrate the superiority of our methods for essence transfer over existing methods for style transfer, domain adaptation, and text-based semantic editing. Our code is available at: https://github.com/hila-chefer/TargetCLIP.

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Acknowledgment

This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant ERC CoG 725974).

Author information

Authors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Hila Chefer, Roni Paiss & Lior Wolf

  2. University of Copenhagen, Copenhagen, Denmark

    Sagie Benaim

Authors
  1. Hila Chefer
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  2. Sagie Benaim
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  3. Roni Paiss
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  4. Lior Wolf
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Corresponding author

Correspondence to Hila Chefer .

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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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Chefer, H., Benaim, S., Paiss, R., Wolf, L. (2022). Image-Based CLIP-Guided Essence Transfer. 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 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_40

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  • DOI: https://doi.org/10.1007/978-3-031-19778-9_40

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