GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration (2412.04440v1)

Abstract: Text-to-video generation models have shown significant progress in the recent years. However, they still struggle with generating complex dynamic scenes based on compositional text prompts, such as attribute binding for multiple objects, temporal dynamics associated with different objects, and interactions between objects. Our key motivation is that complex tasks can be decomposed into simpler ones, each handled by a role-specialized MLLM agent. Multiple agents can collaborate together to achieve collective intelligence for complex goals. We propose GenMAC, an iterative, multi-agent framework that enables compositional text-to-video generation. The collaborative workflow includes three stages: Design, Generation, and Redesign, with an iterative loop between the Generation and Redesign stages to progressively verify and refine the generated videos. The Redesign stage is the most challenging stage that aims to verify the generated videos, suggest corrections, and redesign the text prompts, frame-wise layouts, and guidance scales for the next iteration of generation. To avoid hallucination of a single MLLM agent, we decompose this stage to four sequentially-executed MLLM-based agents: verification agent, suggestion agent, correction agent, and output structuring agent. Furthermore, to tackle diverse scenarios of compositional text-to-video generation, we design a self-routing mechanism to adaptively select the proper correction agent from a collection of correction agents each specialized for one scenario. Extensive experiments demonstrate the effectiveness of GenMAC, achieving state-of-the art performance in compositional text-to-video generation.

• The paper presents GenMAC, a multi-agent framework that decomposes text-to-video generation into iterative Design, Generation, and Redesign stages for improved compositionality.
• It leverages diffusion models and specialized MLLM agents to layout, synthesize, and correct video content, ensuring precise alignment with intricate text prompts.
• Experimental results demonstrate significant gains in spatial, temporal, and action binding, positioning GenMAC ahead of state-of-the-art models.

Compositional Text-to-Video Generation with GenMAC: A Multi-Agent Approach

The paper "0.05 GenMAC: Compositional Text-to-Video Generation with Multi-Agent Collaboration" explores advancements in compositional text-to-video (T2V) generation, addressing the complexities faced by existing models when interpreting intricate prompts. The research introduces a novel framework named GenMAC, an iterative, multi-agent system specifically designed to enhance the compositional capabilities of T2V generation models. This essay provides a detailed review of the paper, emphasizing the core methodologies, technology contributions, experimental outcomes, and implications for future AI developments.

Methodology

GenMAC builds upon the recent strides in diffusion models and aims to overcome their limitations regarding compositionality in T2V generation. The methodology revolves around an iterative multi-agent collaborative workflow consisting of three sequential stages: Design, Generation, and Redesign.

1. Design Stage: This stage employs a Multimodal LLM (MLLM) to establish a high-level structure by generating a layout of objects in frames based on the input text prompt. It sets the baseline for object existence, spatial positioning, and potential interactions within the video.
2. Generation Stage: Utilizing a pre-existing video generation model, this stage synthesizes a video by implementing the structure and guidance provided by the MLLM. Guidance here refers to the bounding box controls and guidance scale, critical for aligning generated content with the initial design blueprint.
3. Redesign Stage: Recognized as the most challenging phase, this stage involves breaking down its complex tasks into simpler sub-tasks handled by specialized MLLM agents. Verification, suggestion, correction, and output structuring agents work sequentially to identify and rectify any discrepancies between the generated video and the original text prompt, fostering continuous improvement.

Key Contributions

The multi-agent design is a central innovation, promoting task decomposition that leads to role specialization among agents. For example, through adaptive self-routing, suitable correction agents are selected from a pool for each distinct compositional challenge, such as maintaining temporal consistency or spatial dynamics. This approach mitigates the singular limitations of an MLLM and enables complex task resolution through collective intelligence.

Experimental Results

The effectiveness of GenMAC was demonstrated through extensive experimentation, outperforming state-of-the-art models on several fronts. Quantitatively, the framework achieved significant improvements across diverse compositional aspects such as consistent and dynamic attribute binding, spatial relationships, motion binding, action binding, object interactions, and generative numeracy. The integration of iterative refinement stages ensures that the generated content progressively adheres more closely to the intricacies of compositional prompts. These results validate both the efficacy of the collaborative multi-agent approach and the adaptability of the framework to various compositional tasks.

Implications and Future Directions

Practically, GenMAC provides a robust solution for generating videos that require precise and complex compositions, a growing demand in our data-driven visual culture. Theoretically, the framework advances the understanding of multi-agent collaboration, particularly through task division and specialization, which can be applied to other realms of AI beyond video generation.

Looking ahead, improvements in MLLMs could further enhance GenMAC’s capabilities. Additionally, exploring other domains where multi-agent collaboration can solve complexities inherent to AI models could yield fruitful results. The combination of role-specialized agents and iterative refinement appears promising in addressing tasks that require nuanced understanding and execution beyond the capabilities of current singular AI models.

In summary, this paper delivers a significant step toward realizing sophisticated compositional text-to-video generation through its multi-agent framework, setting a precedent for more advanced AI systems that can not only generate but also understand and adapt to complex compositional instructions.