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.
|Title of host publication||Computer Vision – ECCV 2020 - 16th European Conference, 2020, Proceedings|
|Editors||Andrea Vedaldi, Horst Bischof, Thomas Brox, Jan-Michael Frahm|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||17|
|Publication status||Published - 2020|
|Event||16th European Conference on Computer Vision, ECCV 2020 - Glasgow, United Kingdom|
Duration: 2020 Aug 23 → 2020 Aug 28
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||16th European Conference on Computer Vision, ECCV 2020|
|Period||20/8/23 → 20/8/28|
Bibliographical noteFunding Information:
supported in part by the NSF CAREER Grant
This work is supported in part by the NSF CAREER Grant #1149783.
© 2020, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)