Summary of Lomoe: Localized Multi-object Editing Via Multi-diffusion, by Goirik Chakrabarty et al.
LoMOE: Localized Multi-Object Editing via Multi-Diffusion
by Goirik Chakrabarty, Aditya Chandrasekar, Ramya Hebbalaguppe, Prathosh AP
First submitted to arxiv on: 1 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel framework for zero-shot localized multi-object editing through a multi-diffusion process enables users to perform various operations on objects within an image, such as adding, replacing, or editing many objects in a complex scene in one pass. This approach leverages foreground masks and corresponding simple text prompts that exert localized influences on the target regions resulting in high-fidelity image editing. The combination of cross-attention and background preservation losses within the latent space ensures that the characteristics of the object being edited are preserved while simultaneously achieving a high-quality, seamless reconstruction of the background with fewer artifacts compared to existing methods. The paper also introduces the LoMOE-Bench dataset dedicated to multi-object editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists developed a new way to edit images by adding, removing, or changing multiple objects in one step. They used a special kind of computer program called a diffusion model that can understand text prompts and make changes to specific parts of an image. This allowed them to create high-quality edited images with minimal artifacts. The team also created a dataset of images for testing this new technique. |
Keywords
* Artificial intelligence * Cross attention * Diffusion * Diffusion model * Latent space * Zero shot
