Optimisation-Based Multi-Modal Semantic Image Editing
Bowen Li Yongxin Yang Steven McDonagh Shifeng Zhang Petru-Daniel Tudosiu Sarah Parisot
Image editing affords increased control over the aesthetics and content of
generated images. Pre-existing works focus predominantly on text-based
instructions to achieve desired image modifications, which limit edit precision
and accuracy. In this work, we propose an inference-time editing optimisation,
designed to extend beyond textual edits to accommodate multiple editing
instruction types (e.g. spatial layout-based; pose, scribbles, edge maps). We
propose to disentangle the editing task into two competing subtasks: successful
local image modifications and global content consistency preservation, where
subtasks are guided through two dedicated loss functions. By allowing to adjust
the influence of each loss function, we build a flexible editing solution that
can be adjusted to user preferences. We evaluate our method using text, pose
and scribble edit conditions, and highlight our ability to achieve complex
edits, through both qualitative and quantitative experiments.