Mora: Enabling Generalist Video Generation via A Multi-Agent Framework

ICLR 2025 Conference Submission4065 Authors

Keywords: Video Generation, Multi-agent, Adaption Training

TL;DR: Mora, a multi-agent framework, outperforms open-source text-to-video methods by using self-modulation, data-free training, and human-in-the-loop filtering, achieving results comparable to OpenAI's Sora on video generation tasks.

Abstract: Text-to-video generation has made significant strides, but replicating the capabilities of advanced systems like OpenAI’s Sora remains challenging due to their closed-source nature. Existing open-source methods struggle to achieve comparable performance, often hindered by ineffective agent collaboration and inadequate training data quality. In this paper, we introduce Mora, a novel multi-agent framework that leverages existing open-source modules to replicate Sora’s functionalities. We address these fundamental limitations by proposing three key techniques: (1) multi-agent fine-tuning with a self-modulation factor to enhance inter-agent coordination, (2) a data-free training strategy that uses large models to synthesize training data, and (3) a human-in-the-loop mechanism combined with multimodal large language models for data filtering to ensure high-quality training datasets. Our comprehensive experiments on six video generation tasks demonstrate that Mora achieves performance comparable to Sora on VBench \cite{huang2024vbench}, outperforming existing open-source methods across various tasks. Specifically, in the text-to-video generation task, Mora achieved a Video Quality score of 0.800, surpassing Sora’s 0.797 and outperforming all other baseline models across six key metrics. Additionally, in the image-to-video generation task, Mora achieved a perfect Dynamic Degree score of 1.00, demonstrating exceptional capability in enhancing motion realism and achieving higher Imaging Quality than Sora. These results highlight the potential of collaborative multi-agent systems and human-in-the-loop mechanisms in advancing text-to-video generation.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 4065