SpaceEdit: Learning a Unified Editing Space for Open-Domain Image Editing
This addresses the need for more intuitive and versatile image editing tools for users, though it is incremental as it builds on existing image-to-image translation frameworks.
The paper tackles the problem of open-domain image editing for color and tone adjustments by proposing a unified model that learns a semantic editing space, enabling tasks like language-guided editing and personalized editing with demonstrated superior performance.
Recently, large pretrained models (e.g., BERT, StyleGAN, CLIP) have shown great knowledge transfer and generalization capability on various downstream tasks within their domains. Inspired by these efforts, in this paper we propose a unified model for open-domain image editing focusing on color and tone adjustment of open-domain images while keeping their original content and structure. Our model learns a unified editing space that is more semantic, intuitive, and easy to manipulate than the operation space (e.g., contrast, brightness, color curve) used in many existing photo editing softwares. Our model belongs to the image-to-image translation framework which consists of an image encoder and decoder, and is trained on pairs of before- and after-images to produce multimodal outputs. We show that by inverting image pairs into latent codes of the learned editing space, our model can be leveraged for various downstream editing tasks such as language-guided image editing, personalized editing, editing-style clustering, retrieval, etc. We extensively study the unique properties of the editing space in experiments and demonstrate superior performance on the aforementioned tasks.