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.
Originalsprog | Engelsk |
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Titel | Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
Antal sider | 5 |
Forlag | IEEE Computer Society Press |
Publikationsdato | 2022 |
Sider | 2225-2229 |
ISBN (Elektronisk) | 9781665487399 |
DOI | |
Status | Udgivet - 2022 |
Begivenhed | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 - New Orleans, USA Varighed: 19 jun. 2022 → 20 jun. 2022 |
Konference
Konference | 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2022 |
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Land | USA |
By | New Orleans |
Periode | 19/06/2022 → 20/06/2022 |
Navn | IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops |
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Vol/bind | 2022-June |
ISSN | 2160-7508 |
Bibliografisk note
Funding Information:
3Acknowledgement: SJB’s work wassupported in partby the Pioneer Centre for AI, DNRF grant number P1. SJB and BL’s work were supported in part by Meta.
Publisher Copyright:
© 2022 IEEE.
ID: 344438935