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BK-Editer: Body-Keeping Text-Conditioned Real Image Editing

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Computational Visual Media (CVM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14592))

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Abstract

With the firestorm of generative macromodelling, text-conditional image editing is a recently emerged and highly useful task with an unlimited future. Although a lot of research progress has been made, most of the methods still fail to achieve editing under body-shape preservation, i.e., they cannot generate results that conform to the semantics of the editing prompt while preserving the body-shape of the original image subject. To address this great challenge, we propose BK-Editer, a method that achieves satisfactory body-shape preservation and accomplishes editing under body shape preservation, which solves two major problems: 1) the edited result matches the corresponding editing prompt, and 2) the edited subject’s body shape is largely the same as the original subject’s body shape. In addition, our method does not require time-consuming training on a large-scale dataset and is a self-supervised method.

This work is supported by Shenzhen Science and Technology Innovation Commission (JSGG20220831105002004, JCYJ20200109114835623).

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Author information

Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Jiancheng Huang, Yifan Liu, Linxiao Shi, Jin Qin & Shifeng Chen

  2. University of Chinese Academy of Sciences, Shenzhen, China

    Jiancheng Huang

  3. Southern University of Science and Technology, Shenzhen, China

    Yifan Liu & Linxiao Shi

Authors
  1. Jiancheng Huang
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  2. Yifan Liu
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  3. Linxiao Shi
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  4. Jin Qin
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  5. Shifeng Chen
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Correspondence to Shifeng Chen .

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Editors and Affiliations

  1. Victoria University of Wellington, Wellington, New Zealand

    Fang-Lue Zhang

  2. Ben-Gurion University, Be'er Sheva, Israel

    Andrei Sharf

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Huang, J., Liu, Y., Shi, L., Qin, J., Chen, S. (2024). BK-Editer: Body-Keeping Text-Conditioned Real Image Editing. In: Zhang, FL., Sharf, A. (eds) Computational Visual Media. CVM 2024. Lecture Notes in Computer Science, vol 14592. Springer, Singapore. https://doi.org/10.1007/978-981-97-2095-8_13

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  • DOI: https://doi.org/10.1007/978-981-97-2095-8_13

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  • Online ISBN: 978-981-97-2095-8

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