Summary of Storynizor: Consistent Story Generation Via Inter-frame Synchronized and Shuffled Id Injection, by Yuhang Ma et al.
Storynizor: Consistent Story Generation via Inter-Frame Synchronized and Shuffled ID Injection
by Yuhang Ma, Wenting Xu, Chaoyi Zhao, Keqiang Sun, Qinfeng Jin, Zeng Zhao, Changjie Fan, Zhipeng Hu
First submitted to arxiv on: 29 Sep 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 The paper introduces Storynizor, a text-to-image diffusion model that generates coherent stories with consistent character poses, backgrounds, and diverse variations. The core innovation lies in its ID-Synchronizer and ID-Injector modules, which utilize auto-mask self-attention and Shuffling Reference Strategy to improve character consistency. The model is trained on the StoryDB dataset, containing 100,000 images of single and multiple characters with detailed descriptions. Experimental results show that Storynizor outperforms other character-specific methods in generating coherent stories with high-fidelity character consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Storynizor is a new computer program that can create stories by combining text and pictures. It’s good at making sure the characters in the story look consistent and natural. The program has two special parts: ID-Synchronizer and ID-Injector. These help make sure the characters are well-dressed, standing in the right places, and doing different things. To learn how to do this, Storynizor uses a large collection of pictures called StoryDB. |
Keywords
» Artificial intelligence » Diffusion model » Mask » Self attention
