Summary of Collaborative Comic Generation: Integrating Visual Narrative Theories with Ai Models For Enhanced Creativity, by Yi-chun Chen and Arnav Jhala
Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity
by Yi-Chun Chen, Arnav Jhala
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper presents a novel visual narrative generative system that combines AI models and human creativity to enhance the comic creation process. The system integrates conceptual principles, comic authoring idioms, and language models to support parts of the generative process, providing a collaborative platform for creating comic content. By translating these principles into system layers, the approach facilitates sequential decision-making in panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into human-AI cooperative comic generation, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study makes it easier to create comics by combining the creativity of humans with the help of computers. The system uses special guidelines called “comic-authoring idioms” that are based on how humans have created comic sequences in the past. These idioms provide guidance on things like what goes into each panel, how to build tension in a story, and how to move from one panel to another. By using AI models to help with these tasks, the system can generate more engaging and coherent comics that are fun for readers. |
Keywords
» Artificial intelligence » Machine learning
