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Context-Consistent Semantic Image Editing with Style-Preserved Modulation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13677))

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Abstract

Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region. A recent work borrows SPADE block to achieve semantic image editing. However, it cannot produce pleasing results due to style discrepancy between the edited region and surrounding pixels. We attribute this to the fact that SPADE only uses an image-independent local semantic layout but ignores the image-specific styles included in the known pixels. To address this issue, we propose a style-preserved modulation (SPM) comprising two modulations processes: The first modulation incorporates the contextual style and semantic layout, and then generates two fused modulation parameters. The second modulation employs the fused parameters to modulate feature maps. By using such two modulations, SPM can inject the given semantic layout while preserving the image-specific context style. Moreover, we design a progressive architecture for generating the edited content in a coarse-to-fine manner. The proposed method can obtain context-consistent results and significantly alleviate the unpleasant boundary between the generated regions and the known pixels.

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Acknowledgement

This work is supported by State Grid Corporation of China (Grant No. 5500-202011091A-0-0-00).

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Authors and Affiliations

  1. Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, Shanghai, China

    Wuyang Luo, Su Yang, Hong Wang & Bo Long

  2. School of Computer Science and Technology, China University of Petroleum, Qingdao, China

    Weishan Zhang

Authors
  1. Wuyang Luo
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  2. Su Yang
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  3. Hong Wang
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  4. Bo Long
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  5. Weishan Zhang
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Corresponding author

Correspondence to Su Yang .

Editor information

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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Luo, W., Yang, S., Wang, H., Long, B., Zhang, W. (2022). Context-Consistent Semantic Image Editing with Style-Preserved Modulation. 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 13677. Springer, Cham. https://doi.org/10.1007/978-3-031-19790-1_34

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  • DOI: https://doi.org/10.1007/978-3-031-19790-1_34

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