GenMAC: Compositional Text-to-Video Generation with Multi-Agent
  Collaboration
Authors: name: Kaiyi Huang, name: Yukun Huang, name: Xuefei Ning, name: Zinan Lin, name: Yu Wang, name: Xihui Liu

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.