A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916295942864896 |
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| author | Shuai, Xincheng Ding, Henghui Ma, Xingjun Tu, Rongcheng Jiang, Yu-Gang Tao, Dacheng |
| author_facet | Shuai, Xincheng Ding, Henghui Ma, Xingjun Tu, Rongcheng Jiang, Yu-Gang Tao, Dacheng |
| contents | Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field of Artificial Intelligence Generative Content (AIGC). Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models, which generate images according to text prompts. These models demonstrate remarkable generative capabilities and have become widely used tools for image editing. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs. In this survey, we provide a comprehensive review of multimodal-guided image editing techniques that leverage T2I diffusion models. First, we define the scope of image editing from a holistic perspective and detail various control signals and editing scenarios. We then propose a unified framework to formalize the editing process, categorizing it into two primary algorithm families. This framework offers a design space for users to achieve specific goals. Subsequently, we present an in-depth analysis of each component within this framework, examining the characteristics and applicable scenarios of different combinations. Given that training-based methods learn to directly map the source image to target one under user guidance, we discuss them separately, and introduce injection schemes of source image in different scenarios. Additionally, we review the application of 2D techniques to video editing, highlighting solutions for inter-frame inconsistency. Finally, we discuss open challenges in the field and suggest potential future research directions. We keep tracing related works at https://github.com/xinchengshuai/Awesome-Image-Editing. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2406_14555 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models Shuai, Xincheng Ding, Henghui Ma, Xingjun Tu, Rongcheng Jiang, Yu-Gang Tao, Dacheng Computer Vision and Pattern Recognition Image editing aims to edit the given synthetic or real image to meet the specific requirements from users. It is widely studied in recent years as a promising and challenging field of Artificial Intelligence Generative Content (AIGC). Recent significant advancement in this field is based on the development of text-to-image (T2I) diffusion models, which generate images according to text prompts. These models demonstrate remarkable generative capabilities and have become widely used tools for image editing. T2I-based image editing methods significantly enhance editing performance and offer a user-friendly interface for modifying content guided by multimodal inputs. In this survey, we provide a comprehensive review of multimodal-guided image editing techniques that leverage T2I diffusion models. First, we define the scope of image editing from a holistic perspective and detail various control signals and editing scenarios. We then propose a unified framework to formalize the editing process, categorizing it into two primary algorithm families. This framework offers a design space for users to achieve specific goals. Subsequently, we present an in-depth analysis of each component within this framework, examining the characteristics and applicable scenarios of different combinations. Given that training-based methods learn to directly map the source image to target one under user guidance, we discuss them separately, and introduce injection schemes of source image in different scenarios. Additionally, we review the application of 2D techniques to video editing, highlighting solutions for inter-frame inconsistency. Finally, we discuss open challenges in the field and suggest potential future research directions. We keep tracing related works at https://github.com/xinchengshuai/Awesome-Image-Editing. |
| title | A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2406.14555 |