Controllable Anime Image Editing via Probability of Attribute Tags

Editing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime ima...

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Published in:Computer graphics forum Vol. 43; no. 7
Main Authors: Song, Zhenghao, Mo, Haoran, Gao, Chengying
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.10.2024
Subjects:
Anime
CCS Concepts
Computing methodologies → Image processing
Controllability
Editing
Image manipulation
Tags
ISSN:0167-7055, 1467-8659
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Abstract Editing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime image editing framework based on adjusting the tag probabilities, in which a probability encoding network (PEN) is developed to encode the probabilities into features that capture continuous characteristic of the probabilities. Thus, the encoded features are able to direct the generative process of a pre‐trained diffusion model and facilitate the linear manipulation. We also introduce a local editing module that automatically identifies the intended regions and constrains the edits to be applied to those regions only, which preserves the others unchanged. Comprehensive comparisons with existing methods indicate the effectiveness of our framework in both one‐shot and linear editing modes. Results in additional applications further demonstrate the generalization ability of our approach.
AbstractList Editing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing methods fall short in the progressive modification and preservation of unintended regions in the input image. We propose a controllable anime image editing framework based on adjusting the tag probabilities, in which a probability encoding network (PEN) is developed to encode the probabilities into features that capture continuous characteristic of the probabilities. Thus, the encoded features are able to direct the generative process of a pre‐trained diffusion model and facilitate the linear manipulation. We also introduce a local editing module that automatically identifies the intended regions and constrains the edits to be applied to those regions only, which preserves the others unchanged. Comprehensive comparisons with existing methods indicate the effectiveness of our framework in both one‐shot and linear editing modes. Results in additional applications further demonstrate the generalization ability of our approach.
Author Gao, Chengying
Mo, Haoran
Song, Zhenghao
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Snippet Editing anime images via probabilities of attribute tags allows controlling the degree of the manipulation in an intuitive and convenient manner. Existing...
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SubjectTerms Anime
CCS Concepts
Computing methodologies → Image processing
Controllability
Editing
Image manipulation
Tags
Title Controllable Anime Image Editing via Probability of Attribute Tags
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