Summary of Multi-view Image Diffusion Via Coordinate Noise and Fourier Attention, by Justin Theiss et al.
Multi-view Image Diffusion via Coordinate Noise and Fourier Attention
by Justin Theiss, Norman Müller, Daeil Kim, Aayush Prakash
First submitted to arxiv on: 4 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 Recently, advancements in text-to-image generation using diffusion models have led to higher fidelity and generalization capabilities compared to previous baselines. However, generating holistic multi-view consistent images from prompts remains an important challenge. To address this, we propose a novel diffusion process that incorporates attention mechanisms for time-dependent spatial frequencies, noise initialization techniques, and cross-attention losses. Our Fourier-based attention block focuses on features from non-overlapping regions of the generated scene to align global appearance. The proposed technique improves state-of-the-art (SOTA) performance on several quantitative metrics with qualitatively better results compared to other SOTA approaches for multi-view consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to generate images from text that looks more realistic and consistent across different views. Right now, this task is challenging because it’s hard to make sure the generated images match the prompt. The authors propose a new method that uses attention mechanisms to focus on specific parts of the image and noise initialization techniques to make the generated images more coherent. This leads to better results compared to previous approaches for generating multi-view consistent images. |
Keywords
» Artificial intelligence » Attention » Cross attention » Diffusion » Generalization » Image generation » Prompt
