Anim-Director: A Large Multimodal Model Powered Agent for Controllable
  Animation Video Generation
Authors: name: Yunxin Li, name: Haoyuan Shi, name: Baotian Hu, name: Longyue Wang, name: Jiashun Zhu, name: Jinyi Xu, name: Zhen Zhao, name: Min Zhang

Traditional animation generation methods depend on training generative models
with human-labelled data, entailing a sophisticated multi-stage pipeline that
demands substantial human effort and incurs high training costs. Due to limited
prompting plans, these methods typically produce brief, information-poor, and
context-incoherent animations. To overcome these limitations and automate the
animation process, we pioneer the introduction of large multimodal models
(LMMs) as the core processor to build an autonomous animation-making agent,
named Anim-Director. This agent mainly harnesses the advanced understanding and
reasoning capabilities of LMMs and generative AI tools to create animated
videos from concise narratives or simple instructions. Specifically, it
operates in three main stages: Firstly, the Anim-Director generates a coherent
storyline from user inputs, followed by a detailed director's script that
encompasses settings of character profiles and interior/exterior descriptions,
and context-coherent scene descriptions that include appearing characters,
interiors or exteriors, and scene events. Secondly, we employ LMMs with the
image generation tool to produce visual images of settings and scenes. These
images are designed to maintain visual consistency across different scenes
using a visual-language prompting method that combines scene descriptions and
images of the appearing character and setting. Thirdly, scene images serve as
the foundation for producing animated videos, with LMMs generating prompts to
guide this process. The whole process is notably autonomous without manual
intervention, as the LMMs interact seamlessly with generative tools to generate
prompts, evaluate visual quality, and select the best one to optimize the final
output.