Summary of Unified Editing Of Panorama, 3d Scenes, and Videos Through Disentangled Self-attention Injection, by Gihyun Kwon et al.
Unified Editing of Panorama, 3D Scenes, and Videos Through Disentangled Self-Attention Injection
by Gihyun Kwon, Jangho Park, Jong Chul Ye
First submitted to arxiv on: 27 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a unified editing framework for text-to-image models, building upon existing methods for single image editing with self-attention injection and video editing with shared attention. The approach utilizes a basic 2D image text-to-image (T2I) diffusion model and designs a sampling method that enables editing consecutive images while maintaining semantic consistency. By incorporating shared self-attention features during both reference and consecutive image sampling processes, the framework can edit across diverse modalities including 3D scenes, videos, and panorama images. The proposed method achieves impressive results in image generation and editing tasks, demonstrating its potential for various applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to edit pictures and videos with just a few clicks. This paper shows how to make this happen by combining two existing techniques into one powerful tool. The new framework uses a basic model that can generate images from text and adds a special way of sampling consecutive images while keeping their meanings consistent. With this method, you can edit not just regular 2D pictures but also 3D scenes, videos, and even panoramic views. This breakthrough has the potential to make editing more efficient and fun for many applications. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Image generation » Self attention
