Summary of Videogen-of-thought: Step-by-step Generating Multi-shot Video with Minimal Manual Intervention, by Mingzhe Zheng et al.
VideoGen-of-Thought: Step-by-step generating multi-shot video with minimal manual intervention
by Mingzhe Zheng, Yongqi Xu, Haojian Huang, Xuran Ma, Yexin Liu, Wenjie Shu, Yatian Pang, Feilong Tang, Qifeng Chen, Harry Yang, Ser-Nam Lim
First submitted to arxiv on: 3 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 Video generation models excel at short clips but struggle with multi-shot narratives due to disjointed visual dynamics and fractured storylines. Existing solutions rely on manual scripting/editing or prioritize single-shot fidelity over cross-scene continuity, limiting their practicality for movie-like content. We introduce VideoGen-of-Thought (VGoT), a step-by-step framework that automates multi-shot video synthesis from a single sentence by addressing three core challenges: narrative fragmentation, visual inconsistency, and transition artifacts. VGoT’s dynamic storyline modeling converts user prompts into concise shot descriptions, elaborating them into detailed specifications across five domains to ensure logical narrative progression with self-validation. The identity-aware cross-shot propagation maintains character fidelity while allowing trait variations dictated by the storyline. Adjacent latent transition mechanisms implement boundary-aware reset strategies for seamless visual flow and narrative continuity. VGoT outperforms state-of-the-art baselines in within-shot face consistency, style consistency, and cross-shot consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Right now, video generation models can only make short videos that don’t have a cohesive storyline. They’re good at making individual scenes, but they don’t work well together to create a movie-like experience. To fix this, we created a new way to generate multi-shot videos called VideoGen-of-Thought (VGoT). It takes just one sentence as input and generates a video with a clear storyline that flows smoothly from one scene to the next. VGoT uses special techniques to keep characters consistent across different scenes and ensure that the story makes sense. This is a big improvement over current methods, which require a lot of manual editing or prioritizing individual scenes over the overall story. |
