“A Survey of Multimodal-Guided Image Editing with Text to Image Diffusion Models” provides a comprehensive overview of the rapidly evolving field of multimodal-guided image editing using text-to-image diffusion models. This survey aims to bridge the gap between the advancements in text-to-image generation and their application in image editing tasks.
The authors begin by introducing the concept of multimodal-guided image editing, which involves using textual descriptions to guide the modification of existing images. They highlight the potential of this approach in enabling more intuitive and user-friendly image editing tools, as users can express their desired changes through natural language instructions. The survey then delves into the technical foundations of text-to-image diffusion models, which have emerged as a powerful framework for generating high-quality images from textual descriptions. These models learn to map textual representations to visual features through a diffusion process, allowing for the generation of diverse and realistic images.
The paper provides a systematic categorization of existing multimodal-guided image editing methods based on their underlying techniques and application domains. The authors discuss various approaches, including direct editing, where the diffusion model directly modifies the image based on the text input, and latent space manipulation, where the editing is performed in a learned latent representation space.
The survey also covers the evaluation metrics and datasets commonly used in assessing the performance of multimodal-guided image editing methods. The authors emphasize the importance of considering both the visual quality of the edited images and their alignment with the textual instructions. Furthermore, the paper highlights the challenges and future directions in this field. The authors discuss the need for more diverse and large-scale datasets, improved evaluation protocols, and the development of more interactive and user-friendly editing interfaces. “A Survey of Multimodal-Guided Image Editing with Text-to-Image Diffusion Models” provides a timely and comprehensive overview of an emerging research area. By bridging the gap between text-to-image generation and image editing, this survey contributes to the development of more advanced and intuitive image editing tools. The insights and future directions discussed in the paper are valuable for researchers and practitioners working on multimodal image editing and related fields.
