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Modeling Artistic Workflows for Image Generation and Editing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12363)

Abstract

People often create art by following an artistic workflow involving multiple stages that inform the overall design. If an artist wishes to modify an earlier decision, significant work may be required to propagate this new decision forward to the final artwork. Motivated by the above observations, we propose a generative model that follows a given artistic workflow, enabling both multi-stage image generation as well as multi-stage image editing of an existing piece of art. Furthermore, for the editing scenario, we introduce an optimization process along with learning-based regularization to ensure the edited image produced by the model closely aligns with the originally provided image. Qualitative and quantitative results on three different artistic datasets demonstrate the effectiveness of the proposed framework on both image generation and editing tasks.

Notes

Acknowledgements

This work is supported in part by the NSF CAREER Grant #1149783.

Supplementary material

504473_1_En_10_MOESM1_ESM.pdf (6.2 mb)
Supplementary material 1 (pdf 6370 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CaliforniaOaklandUSA
  2. 2.Adobe ResearchSan JoseUSA

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