Neural Image Recolorization for Creative Domains
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We present a self-supervised approach to recolorization of images from design-oriented domains. Our approach can recolor images based on image exemplars or target color palettes provided by a user. In contrast with previous approaches, our method can reproduce color palettes with luminance distributions that differ significantly from input, and our method is the first palette-based approach to distinguish between recolorings that match reflectance and those that match illumination, making it particularly well-suited to visualizing different aesthetic decisions in design applications. The key to our approach is first to learn latent representations for texture and color in a setting where self-supervision is especially straightforward, and then to learn a mapping to our color representation from input color palettes and scene illumination, which offers a more intuitive space for controlling and exploring recolorization.
Original language | English |
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Title of host publication | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
Number of pages | 5 |
Publisher | IEEE Computer Society Press |
Publication date | 2022 |
Pages | 2225-2229 |
ISBN (Electronic) | 9781665487399 |
DOIs | |
Publication status | Published - 2022 |
Event | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, United States Duration: 19 Jun 2022 → 20 Jun 2022 |
Conference
Conference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
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Land | United States |
By | New Orleans |
Periode | 19/06/2022 → 20/06/2022 |
Series | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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Volume | 2022-June |
ISSN | 2160-7508 |
Bibliographical note
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© 2022 IEEE.
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