Modeling Artistic Workflows for Image Generation and Editing

• URL: http://arxiv.org/abs/2007.07238v1
• Date: Tue, 14 Jul 2020 17:54:26 GMT
• Title: Modeling Artistic Workflows for Image Generation and Editing
• Authors: Hung-Yu Tseng, Matthew Fisher, Jingwan Lu, Yijun Li, Vladimir Kim, Ming-Hsuan Yang
• Abstract summary: We propose a generative model that follows a given artistic workflow. It enables both multi-stage image generation as well as multi-stage image editing of an existing piece of art.
• Score: 83.43047077223947
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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