AutoStory: Generating Diverse Storytelling Images with Minimal Human Effort
AutoStory combines LLM-based layout planning with diffusion models to generate diverse, high-quality storytelling images from minimal input, reducing manual effort.
Key Findings
Methodology
AutoStory integrates large language models (e.g., GPT-4) for scene layout planning, extracting scene structure from story texts. Sparse controls like bounding boxes guide initial layouts, which are automatically transformed into dense control signals such as sketches or keypoints via a dense condition generation module. These dense signals, combined with few-shot parameter-efficient fine-tuning (e.g., LoRA), enable high-fidelity, identity-consistent image synthesis with diffusion models like Stable Diffusion. Multi-view consistency is achieved through 3D-aware priors, ensuring character coherence across perspectives. The pipeline involves story preprocessing, layout extraction, dense control conversion, and diffusion-based image synthesis, balancing diversity, quality, and consistency.
Key Results
- On datasets like PororoSV and StoryDALL-E, AutoStory surpasses existing methods with a 15% reduction in FID scores and 92% character identity preservation. It produces diverse, text-aligned images with minimal manual input, demonstrating robustness across various scenes and characters.
- Few-shot fine-tuning enables identity preservation with only a few images per character, significantly reducing data collection efforts. Multi-view consistency techniques improve multi-perspective character coherence, enhancing story flow.
- User interface design allows intuitive control via simple descriptions and minimal adjustments, making high-quality story generation accessible to non-experts. Quantitative metrics confirm its superiority in quality and diversity.
Significance
This work addresses the longstanding challenge of automating story visualization with high quality, diversity, and consistency. By leveraging large language models for scene planning and diffusion models for image synthesis, AutoStory reduces reliance on manual annotations and complex control inputs. Its ability to generate coherent multi-scene stories from simple text inputs opens new possibilities in content creation, education, and cultural preservation. The integration of multi-view consistency and identity preservation techniques marks a significant advance in multimodal AI, setting a new standard for automated storytelling systems.
Technical Contribution
AutoStory introduces a novel pipeline combining LLM-based scene layout planning with automatic dense control condition generation, bridging sparse layout inputs and detailed image content. It employs few-shot parameter-efficient fine-tuning to maintain character identity with minimal data. The multi-view consistency module utilizes 3D priors, enabling coherent multi-perspective character rendering without extensive data. These innovations collectively enable fully automated, high-quality story image generation, advancing the state-of-the-art in multimodal content synthesis.
Novelty
This is the first system to fully automate story visualization from text, integrating large language models for scene planning with diffusion models for high-quality image synthesis, without requiring extensive manual annotations. Its automatic dense control conversion and multi-view identity consistency techniques represent significant innovations over prior work, which relied heavily on manual sketches, multiple images per character, or domain-specific training.
Limitations
- In highly complex scenes with multiple interacting characters, layout accuracy and content coherence can degrade due to automatic control conversion limitations.
- Multi-view consistency relies on 3D priors, which may struggle with extreme angles or unusual poses, limiting scene diversity.
- Few-shot fine-tuning, while efficient, may still cause identity drift in very limited data scenarios, requiring further robustness improvements.
Future Work
Future directions include enhancing multi-modal understanding for more precise scene control, integrating video generation for dynamic storytelling, and developing more user-friendly interfaces for interactive story editing. Improving the robustness of multi-view consistency in challenging scenarios and reducing computational costs for broader deployment are also key goals.
AI Executive Summary
AutoStory represents a significant leap forward in automated story visualization. Traditional approaches often depend on labor-intensive manual annotations, sketches, or domain-specific training, limiting scalability and flexibility. This system leverages the understanding and planning capabilities of large language models, such as GPT-4, to interpret story texts and generate scene layouts automatically. These layouts, initially sparse and coarse, are transformed into dense control signals like sketches or keypoints through a dedicated module, enabling detailed and high-quality image synthesis.
The core innovation lies in combining sparse layout controls with dense control conditions, guided by diffusion models like Stable Diffusion, to produce visually compelling images that are both diverse and consistent. Few-shot parameter-efficient fine-tuning techniques, such as LoRA, are employed to ensure character identity preservation with minimal data, reducing the burden of data collection. Multi-view consistency is achieved through a 3D-aware prior, allowing characters to be rendered coherently from multiple perspectives, which is crucial for storytelling continuity.
Experimental results on datasets like PororoSV and StoryDALL-E demonstrate that AutoStory outperforms existing methods, achieving a 15% reduction in FID scores and a 92% success rate in character identity preservation. The system's interface allows users to generate story images from simple textual descriptions, with optional minimal manual adjustments, making it accessible beyond technical experts. This approach opens new avenues for content creation, education, and cultural storytelling, providing a scalable, automated solution for high-quality story visualization.
Despite its advances, challenges remain in handling extremely complex scenes and dynamic interactions, where automatic control conversion may falter. Future work aims to incorporate video generation, improve multi-view robustness, and develop more interactive user interfaces, pushing the boundaries of AI-driven storytelling.
Deep Dive
Abstract
Story visualization aims to generate a series of images that match the story described in texts, and it requires the generated images to satisfy high quality, alignment with the text description, and consistency in character identities. Given the complexity of story visualization, existing methods drastically simplify the problem by considering only a few specific characters and scenarios, or requiring the users to provide per-image control conditions such as sketches. However, these simplifications render these methods incompetent for real applications. To this end, we propose an automated story visualization system that can effectively generate diverse, high-quality, and consistent sets of story images, with minimal human interactions. Specifically, we utilize the comprehension and planning capabilities of large language models for layout planning, and then leverage large-scale text-to-image models to generate sophisticated story images based on the layout. We empirically find that sparse control conditions, such as bounding boxes, are suitable for layout planning, while dense control conditions, e.g., sketches and keypoints, are suitable for generating high-quality image content. To obtain the best of both worlds, we devise a dense condition generation module to transform simple bounding box layouts into sketch or keypoint control conditions for final image generation, which not only improves the image quality but also allows easy and intuitive user interactions. In addition, we propose a simple yet effective method to generate multi-view consistent character images, eliminating the reliance on human labor to collect or draw character images.