Modeling Artistic Workflows for Image Generation and Editing
This addresses the challenge for artists who need to efficiently modify earlier decisions in their creative process, though it appears incremental in applying existing generative methods to a specific domain.
The paper tackles the problem of propagating changes through multi-stage artistic workflows by proposing a generative model that enables both multi-stage image generation and editing, with results validated on three artistic datasets.
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.