Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation
Generate coherent storytelling videos using retrieval-augmented video generation.
Key Findings
Methodology
This study proposes a framework consisting of two modules: Motion Structure Retrieval and Structure-Guided Text-to-Video Synthesis. First, video candidates are retrieved using a text retrieval system, and their depth information is extracted as motion structure. Then, a controllable video generation model uses this structure and text prompts to generate videos.
Key Results
- Experiments show the method significantly outperforms existing baselines in video generation performance, with an X% improvement on a specific dataset.
- The personalization method TimeInv excels in maintaining character consistency, outperforming existing competitors.
- Ablation studies confirm the structural guidance module's contribution to generation quality.
Significance
By leveraging existing video resources, this study significantly lowers the barrier to generating high-quality storytelling videos. It addresses the challenges of layout and composition control in traditional methods, providing a more efficient tool for content creators.
Technical Contribution
Technical contributions include introducing a retrieval-augmented video generation paradigm, proposing an adjustable structure-guided text-to-video model, and developing a new concept personalization method, TimeInv.
Novelty
This is the first to introduce retrieval-augmented video generation, combining structural guidance and personalized generation, significantly enhancing video quality and consistency.
Limitations
- Generated videos may still exhibit character inconsistency in complex scenarios.
- Dependence on video databases may limit diversity.
Future Work
Future work could explore more complex scene generation and enhance diversity and quality using larger datasets.
AI Executive Summary
Generating visual storytelling videos typically requires live-action filming or animation rendering, a complex and time-consuming process. To address this, researchers propose a novel method using existing video clips to create coherent storytelling videos. The method is realized through two core modules: Motion Structure Retrieval and Structure-Guided Text-to-Video Synthesis. First, video candidates are retrieved using a text retrieval system, and their depth information is extracted as motion structure. Then, a controllable video generation model uses this structure and text prompts to generate videos. Experimental results show the method significantly outperforms existing baselines, particularly in character consistency and generation quality. However, the method still has limitations in complex scenarios, and future work will continue to optimize generation quality and expand application scenarios.
Deep Analysis
Background
Video generation technology has made significant progress in recent years, especially in text-to-video generation. However, existing methods still have limitations in controlling layout and composition, making it difficult to meet the needs of filmmaking.
Core Problem
Generating high-quality visual storytelling videos requires solving layout and composition control issues while ensuring character consistency and generation quality.
Innovation
The core innovation of this study is the introduction of a retrieval-augmented video generation paradigm, combining structural guidance and personalized generation to significantly enhance video quality and consistency.
Methodology
- �� Motion Structure Retrieval: Video candidates are retrieved using a text retrieval system, and depth information is extracted as motion structure.
- �� Structure-Guided Text-to-Video Synthesis: A controllable video generation model uses this structure and text prompts to generate videos.
- �� Personalized Generation: The TimeInv method ensures character consistency.
Experiments
Experiments were conducted using multiple datasets, comparing the performance of different baseline methods, with a focus on generation quality and character consistency.
Results
Experimental results show the method significantly outperforms existing baselines, particularly in character consistency and generation quality.
Applications
This method can be used in filmmaking, advertising, and other fields, lowering the barrier to generating high-quality storytelling videos.
Limitations & Outlook
Although effective, the method still has limitations in complex scenarios, and future work will continue to optimize generation quality and expand application scenarios.
Plain Language Accessible to non-experts
Imagine you're in a kitchen cooking. You have many ingredients (video clips) but don't know how to make a good dish (story video). This method is like a smart chef who helps you choose the right ingredients from the fridge and then cooks a delicious meal based on your taste (text prompts). Even if you're not a great cook, it helps you make a great meal.
ELI14 Explained like you're 14
Imagine you're playing a game with lots of characters and scenes to choose from, but you don't know how to make an interesting story. This method is like a game assistant that helps you pick the right characters and scenes and then generates an exciting game story based on your ideas (text prompts). Even if you're not great at storytelling, it helps you create an amazing adventure.
Glossary
Motion Structure Retrieval
Retrieves video candidates using a text retrieval system and extracts their depth information as motion structure.
Used to select suitable video clips as the basis for generation.
Structure-Guided Text-to-Video Synthesis
A controllable video generation model uses motion structure and text prompts to generate videos.
Used to generate videos that match the storyline.
TimeInv
A personalization method ensuring character consistency across different video clips.
Used to address character consistency issues.
Video Diffusion Model
A generative model that produces high-quality videos through gradual denoising.
Core technology for video generation.
Personalized Generation
Specifies character identities through text prompts to ensure character consistency in videos.
Ensures character consistency in generated videos.
Open Questions Unanswered questions from this research
- 1 How to maintain character consistency in more complex scenarios?
- 2 How to enhance video diversity and quality using larger datasets?
Applications
Immediate Applications
Filmmaking
Lowers the barrier to generating high-quality storytelling videos in filmmaking, saving time and cost.
Advertising
Provides more efficient tools for advertisers to generate brand story videos.
Long-term Vision
Virtual Reality
Generates immersive storytelling experiences in virtual reality, enhancing user engagement and interactivity.
Abstract
Generating videos for visual storytelling can be a tedious and complex process that typically requires either live-action filming or graphics animation rendering. To bypass these challenges, our key idea is to utilize the abundance of existing video clips and synthesize a coherent storytelling video by customizing their appearances. We achieve this by developing a framework comprised of two functional modules: (i) Motion Structure Retrieval, which provides video candidates with desired scene or motion context described by query texts, and (ii) Structure-Guided Text-to-Video Synthesis, which generates plot-aligned videos under the guidance of motion structure and text prompts. For the first module, we leverage an off-the-shelf video retrieval system and extract video depths as motion structure. For the second module, we propose a controllable video generation model that offers flexible controls over structure and characters. The videos are synthesized by following the structural guidance and appearance instruction. To ensure visual consistency across clips, we propose an effective concept personalization approach, which allows the specification of the desired character identities through text prompts. Extensive experiments demonstrate that our approach exhibits significant advantages over various existing baselines.