Summary of Generating Compositional Scenes Via Text-to-image Rgba Instance Generation, by Alessandro Fontanella et al.
Generating Compositional Scenes via Text-to-image RGBA Instance Generation
by Alessandro Fontanella, Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Sarah Parisot
First submitted to arxiv on: 16 Nov 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 The proposed novel multi-stage generation paradigm is designed for fine-grained control, flexibility, and interactivity in text-to-image diffusion generative models. To achieve this, the approach introduces a novel training paradigm that adapts a diffusion model to generate isolated scene components as RGBA images with transparency information. This allows for precise control over object attributes, such as instance-level attributes, relative positioning in 3D space, and scene manipulation abilities. The multi-layer composite generation process then smoothly assembles these pre-generated instances into realistic scenes. Experimental results show that the proposed approach can generate diverse and high-quality images with fine-grained control over object appearance and location. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to create images using text prompts is being developed. This method allows for greater control over what’s in the image, where things are located, and how they look. The current best approaches require a lot of work to set up and don’t give you much control. The proposed approach fixes these issues by breaking down the image generation process into smaller steps that can be controlled individually. This makes it possible to create highly complex images from simple text prompts. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation
