ImEW: A Framework for Editing Image in the Wild
Tasnim Mohiuddin, Tianyi Zhang, Maowen Nie, Jing Huang, Qianqian Chen, Wei Shi · 2023
The ability to edit images in a realistic and visually appealing manner is a fundamental requirement in various computer vision applications. In this paper, we present ImEW, a unified framework designed for solving image editing tasks. ImEW utilizes off-the-shelf foundation models to address four essential editing tasks: object removal, object translation, object replacement, and generative fill beyond the image frame. These tasks are accomplished by leveraging the capabilities of state-of-the-art foundation models, namely the Segment Anything Model, Grounding DINO, LaMa, and Stable Diffusion. These models have undergone extensive training on large-scale datasets and have exhibited exceptional performance in understanding image context, object manipulation, and texture synthesis. Through extensive experimentation, we demonstrate the effectiveness and versatility of ImEW in accomplishing image editing tasks across a wide range of real-world scenarios. The proposed framework opens up new possibilities for realistic and visually appealing image editing and enables diverse applications requiring sophisticated image modifications. Additionally, we discuss the limitations and outline potential directions for future research in the field of image editing using off-the-shelf foundation models, enabling continued advancements in this domain.