Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity
- URL: http://arxiv.org/abs/2409.17263v1
- Date: Wed, 25 Sep 2024 18:21:01 GMT
- Title: Collaborative Comic Generation: Integrating Visual Narrative Theories with AI Models for Enhanced Creativity
- Authors: Yi-Chun Chen, Arnav Jhala,
- Abstract summary: This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process.
Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences.
- Score: 1.1181151748260076
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This study presents a theory-inspired visual narrative generative system that integrates conceptual principles-comic authoring idioms-with generative and language models to enhance the comic creation process. Our system combines human creativity with AI models to support parts of the generative process, providing a collaborative platform for creating comic content. These comic-authoring idioms, derived from prior human-created image sequences, serve as guidelines for crafting and refining storytelling. The system translates these principles into system layers that facilitate comic creation through sequential decision-making, addressing narrative elements such as panel composition, story tension changes, and panel transitions. Key contributions include integrating machine learning models into the human-AI cooperative comic generation process, deploying abstract narrative theories into AI-driven comic creation, and a customizable tool for narrative-driven image sequences. This approach improves narrative elements in generated image sequences and engages human creativity in an AI-generative process of comics. We open-source the code at https://github.com/RimiChen/Collaborative_Comic_Generation.
Related papers
- StoryAgent: Customized Storytelling Video Generation via Multi-Agent Collaboration [88.94832383850533]
We propose a multi-agent framework designed for Customized Storytelling Video Generation (CSVG)
StoryAgent decomposes CSVG into distinct subtasks assigned to specialized agents, mirroring the professional production process.
Specifically, we introduce a customized Image-to-Video (I2V) method, LoRA-BE, to enhance intra-shot temporal consistency.
Our contributions include the introduction of StoryAgent, a versatile framework for video generation tasks, and novel techniques for preserving protagonist consistency.
arXiv Detail & Related papers (2024-11-07T18:00:33Z) - A Character-Centric Creative Story Generation via Imagination [15.345466372805516]
We introduce a novel story generation framework called CCI (Character-centric Creative story generation via Imagination)
CCI features two modules for creative story generation: IG (Image-Guided Imagination) and MW (Multi-Writer model)
In the IG module, we utilize a text-to-image model to create visual representations of key story elements, such as characters, backgrounds, and main plots.
The MW module uses these story elements to generate multiple persona-description candidates and selects the best one to insert into the story, thereby enhancing the richness and depth of the narrative.
arXiv Detail & Related papers (2024-09-25T06:54:29Z) - Imagining from Images with an AI Storytelling Tool [0.27309692684728604]
The proposed method explores the multimodal capabilities of GPT-4o to interpret visual content and create engaging stories.
The method is supported by a fully implemented tool, called ImageTeller, which accepts images from diverse sources as input.
arXiv Detail & Related papers (2024-08-21T10:49:15Z) - A Customizable Generator for Comic-Style Visual Narrative [1.320904960556043]
We present a theory-inspired visual narrative generator that incorporates comic-authoring idioms.
The generator creates comics through sequential decision-making across layers from panel composition, object positions, panel transitions, and narrative elements.
arXiv Detail & Related papers (2023-12-14T03:46:30Z) - SketchDreamer: Interactive Text-Augmented Creative Sketch Ideation [111.2195741547517]
We present a method to generate controlled sketches using a text-conditioned diffusion model trained on pixel representations of images.
Our objective is to empower non-professional users to create sketches and, through a series of optimisation processes, transform a narrative into a storyboard.
arXiv Detail & Related papers (2023-08-27T19:44:44Z) - Intelligent Grimm -- Open-ended Visual Storytelling via Latent Diffusion
Models [70.86603627188519]
We focus on a novel, yet challenging task of generating a coherent image sequence based on a given storyline, denoted as open-ended visual storytelling.
We propose a learning-based auto-regressive image generation model, termed as StoryGen, with a novel vision-language context module.
We show StoryGen can generalize to unseen characters without any optimization, and generate image sequences with coherent content and consistent character.
arXiv Detail & Related papers (2023-06-01T17:58:50Z) - ViNTER: Image Narrative Generation with Emotion-Arc-Aware Transformer [59.05857591535986]
We propose a model called ViNTER to generate image narratives that focus on time series representing varying emotions as "emotion arcs"
We present experimental results of both manual and automatic evaluations.
arXiv Detail & Related papers (2022-02-15T10:53:08Z) - Telling Creative Stories Using Generative Visual Aids [52.623545341588304]
We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt.
Compared to a control group, writers who used the visuals as story writing aid wrote significantly more creative, original, complete and visualizable stories.
Findings indicate that cross modality inputs by AI can benefit divergent aspects of creativity in human-AI co-creation, but hinders convergent thinking.
arXiv Detail & Related papers (2021-10-27T23:13:47Z) - FairyTailor: A Multimodal Generative Framework for Storytelling [33.39639788612019]
We introduce a system and a demo, FairyTailor, for human-in-the-loop visual story co-creation.
Users can create a cohesive children's fairytale by weaving generated texts and retrieved images with their input.
To our knowledge, this is the first dynamic tool for multimodal story generation that allows interactive co-formation of both texts and images.
arXiv Detail & Related papers (2021-07-13T02:45:08Z) - Improving Generation and Evaluation of Visual Stories via Semantic
Consistency [72.00815192668193]
Given a series of natural language captions, an agent must generate a sequence of images that correspond to the captions.
Prior work has introduced recurrent generative models which outperform synthesis text-to-image models on this task.
We present a number of improvements to prior modeling approaches, including the addition of a dual learning framework.
arXiv Detail & Related papers (2021-05-20T20:42:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.