Summary of Dreamstory: Open-domain Story Visualization by Llm-guided Multi-subject Consistent Diffusion, By Huiguo He et al.
DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion
by Huiguo He, Huan Yang, Zixi Tuo, Yuan Zhou, Qiuyue Wang, Yuhang Zhang, Zeyu Liu, Wenhao Huang, Hongyang Chao, Jian Yin
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Multimedia (cs.MM)
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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 DreamStory is an open-domain story visualization framework that leverages large language models (LLMs) and a novel multi-subject consistent diffusion model. The framework consists of two components: an LLM acting as a story director, generating descriptive prompts for subjects and scenes aligned with the story; and an innovative Multi-Subject consistent Diffusion model (MSD) for generating consistent multi-subject across images. DreamStory employs multimodal anchors to ensure appearance and semantic consistency with reference images and text. The framework includes Masked Mutual Self-Attention (MMSA) and Masked Mutual Cross-Attention (MMCA) modules, which utilize masking mechanisms to prevent subject blending. DreamStory is evaluated using the DS-500 benchmark, assessing overall performance, subject-identification accuracy, and consistency of the generation model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine creating a movie or video based on a story. This paper presents a new way to do this using computers. It’s called DreamStory. The system uses large language models to understand what’s happening in the story and then generates images that match the narrative. The goal is to make the images look like they were taken from a real movie or TV show. The researchers developed a special model that can create multiple characters in a scene, making sure they all fit together well. They tested their system with a benchmark dataset called DS-500. |
Keywords
» Artificial intelligence » Cross attention » Diffusion model » Self attention
