VideoGen-Agent: Reinforcing Video Generation Agents
VideoGen-Agent enhances video generation via multitask reinforcement learning, improving VABench score by 19.1 points.
Key Findings
Methodology
VideoGen-Agent employs multitask agentic reinforcement learning to coordinate augmentation, generation, and verification tools through multi-turn interactions. It is trained on a category-balanced dataset, first establishing tool-use behavior via supervised fine-tuning, then refining through reinforcement learning. A category-aware hybrid reward evaluates tool-call validity, task-appropriate tool use, and generated video quality.
Key Results
- VideoGen-Agent improved its base text-to-video generator by 19.1 points on the VABench benchmark, from 56.5 to 75.6.
- Upgrading the generation tools further increased the score to 86.1 without additional training.
- Human raters preferred the upgraded configuration over the strongest standalone baseline in 84.3% of comparisons.
Significance
VideoGen-Agent significantly enhances video generation quality by learning tool use, addressing limitations in specialized knowledge, identity preservation, and physical consistency. This study provides new insights into video generation, showcasing the potential of multitask reinforcement learning in complex generation tasks.
Technical Contribution
VideoGen-Agent automates tool use through multitask reinforcement learning, offering a novel video generation framework. It learns across multiple tasks and allows tool upgrades without redesigning workflows, demonstrating flexibility and scalability.
Novelty
VideoGen-Agent is the first multimodal agent to achieve tool use through multitask reinforcement learning, effectively coordinating various tools in video generation, addressing limitations in existing models for complex tasks.
Limitations
- In complex scenarios, tool call accuracy may be insufficient, affecting generation quality.
- Requires substantial training data and computational resources for efficient tool use.
Future Work
Future research could explore more complex tasks and tool integration to further enhance generation quality and efficiency, extending to other multimodal generation tasks.
AI Executive Summary
Recent advances in video generation have made significant strides, yet existing models struggle with tasks requiring specialized knowledge, identity preservation, and physical consistency. VideoGen-Agent enhances video generation quality through multitask agentic reinforcement learning, utilizing external tools. It performs exceptionally on the VABench benchmark, improving scores by 19.1 points, and further increases to 86.1 with upgraded tools. Experimental results demonstrate its advantages in handling complex generation tasks.
The core of VideoGen-Agent lies in its multimodal agent architecture, coordinating augmentation, generation, and verification tools through multi-turn interactions. Trained on a category-balanced dataset, it establishes tool-use behavior via supervised fine-tuning and optimizes through reinforcement learning. The introduced category-aware hybrid reward mechanism effectively evaluates tool-call validity and generated video quality.
Despite its strong performance across tasks, VideoGen-Agent has limitations, such as potential inaccuracies in tool calls in complex scenarios. Future research could explore more complex tasks and tool integration to further enhance generation quality and efficiency.
Deep Analysis
Background
Video generation has seen significant progress, especially in generating high-fidelity, temporally coherent videos. However, existing models struggle with tasks requiring specialized knowledge, identity preservation, and physical consistency, limiting their potential in practical applications like product demonstrations, educational content, and sports analysis.
Core Problem
Current video generation models often fail to accurately capture content specified in prompts, especially when dealing with specialized knowledge, identity preservation, and physical consistency. These issues affect the models' effectiveness in real-world applications.
Innovation
VideoGen-Agent automates tool use through multitask agentic reinforcement learning, effectively coordinating various tools in video generation. This method improves generation quality by addressing limitations in existing models for complex tasks.
Methodology
- �� Trained on a category-balanced dataset
- �� Establishes tool-use behavior via supervised fine-tuning
- �� Optimizes tool use strategy through reinforcement learning
- �� Introduces category-aware hybrid reward mechanism to evaluate tool-call validity and video quality
Experiments
Experiments were conducted on the VABench benchmark, covering six task categories: procedural knowledge, single-entity identity, multi-entity identity, physics simulation, compositional scene, and multi-shot. Generated videos were evaluated using category-specific VLM rubrics, and alignment with human preferences was validated.
Results
VideoGen-Agent improved its base text-to-video generator by 19.1 points on the VABench benchmark, and further increased to 86.1 with upgraded tools. Human raters preferred the upgraded configuration in 84.3% of comparisons.
Applications
VideoGen-Agent can be applied in scenarios requiring high-quality video generation, such as product demonstrations, educational content creation, and film pre-visualization, significantly improving video quality and consistency.
Limitations & Outlook
Despite strong performance, VideoGen-Agent may have insufficient tool call accuracy in complex scenarios, affecting generation quality. Additionally, it requires substantial training data and computational resources.
Plain Language Accessible to non-experts
Imagine you're in a kitchen, and VideoGen-Agent is like a multi-functional chef assistant. It selects the right tools based on your instructions, like a chopper, blender, and oven, to help you create a complex dish. This assistant not only chooses tools based on the recipe but also adjusts steps based on feedback, ensuring each part is executed perfectly.
ELI14 Explained like you're 14
Imagine you're playing a super cool game, and VideoGen-Agent is like your game assistant. It helps you choose the right tools and skills to complete various tasks. For example, if you need to build a castle in the game, it helps you select the right materials and tools to ensure the castle is strong and beautiful. Isn't that awesome?
Glossary
VideoGen-Agent
A multimodal agent that uses multitask reinforcement learning to enhance video generation quality by automating tool use.
Used in the paper to coordinate tools for generating high-quality videos.
VABench
A benchmark consisting of 600 prompts used to evaluate the capabilities of video generation models.
Used to assess VideoGen-Agent's performance across different task categories.
Reinforcement Learning
A machine learning method that optimizes an agent's decision-making through reward and punishment mechanisms.
Used to optimize VideoGen-Agent's tool use strategy.
Multimodal
A processing method involving multiple data types or input modes.
VideoGen-Agent coordinates tool use through multimodal inputs.
Tool Use
The process of selecting and applying appropriate tools in specific tasks to achieve goals.
VideoGen-Agent enhances video generation quality through tool use.
Open Questions Unanswered questions from this research
- 1 How to improve tool call accuracy and efficiency in more complex scenarios.
- 2 Optimizing training data and computational resource usage under limited resources.
Applications
Immediate Applications
Educational Content Creation
Utilize VideoGen-Agent to generate high-quality educational videos, enhancing learning outcomes.
Long-term Vision
Film Pre-Visualization
Apply VideoGen-Agent in film production to enhance pre-visualization effects and production efficiency.
Abstract
Recent advances in video generative models have enabled high-fidelity, temporally coherent video generation. However, these models often struggle to satisfy prompts requiring specialized knowledge, specific identities, physical consistency, or ordered events. In this paper, we present VideoGen-Agent, a multimodal agent trained through multitask agentic reinforcement learning to use external tools for video generation. The agent coordinates augmentation, generation, and verification tools through multi-turn interactions, using the prompt and intermediate observations to guide its decisions. We train a shared policy on a category-balanced dataset spanning six tasks. Supervised fine-tuning on teacher-generated trajectories establishes tool-use behavior, which is then refined through reinforcement learning. A category-aware hybrid reward evaluates tool-call validity, task-appropriate tool use, and generated video quality. We further introduce VABench, a held-out benchmark of 600 prompts covering procedural knowledge, single- and multi-entity identity preservation, physical consistency, scene composition, and multi-shot temporal structure. On VABench, VideoGen-Agent improves over its base text-to-video generator by 19.1 points, from 56.5 to 75.6. Upgrading the generation tools further raises the score to 86.1 without additional agent training. Human raters prefer the upgraded configuration over the strongest standalone baseline in 84.3% of comparisons. These results support learning tool use across video-generation tasks and show that the trained agent can benefit from subsequent advances in generation tools.