Summary of Salient Object-aware Background Generation Using Text-guided Diffusion Models, by Amir Erfan Eshratifar et al.
Salient Object-Aware Background Generation using Text-Guided Diffusion Models
by Amir Erfan Eshratifar, Joao V. B. Soares, Kapil Thadani, Shaunak Mishra, Mikhail Kuznetsov, Yueh-Ning Ku, Paloma de Juan
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 A novel model is introduced for adapting diffusion models to the task of background scene generation for salient objects. This task, also known as text-conditioned outpainting, aims to extend image content beyond a subject’s boundaries on a blank background. The proposed approach uses Stable Diffusion and ControlNet architectures to adapt popular inpainting models for this specific task. The model is evaluated using qualitative and quantitative results across multiple datasets, including a newly proposed metric to measure object expansion without human labeling. Compared to the original Stable Diffusion 2.0 Inpainting model, the adapted approach reduces object expansion by 3.6x on average while maintaining standard visual metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to create background scenes for important objects. Imagine you want to put a person in a scene or add some objects to an image. The goal is to make it look like they are really there. Current methods can’t do this well because they are designed to fill in missing parts of an image, not create a whole new scene. This paper shows how to adapt these methods to make them better at creating background scenes. It also proposes a new way to measure how well the method works without needing humans to label everything. |
Keywords
» Artificial intelligence » Diffusion
