🔍 See our newest Video Generation paper: "Mora: Enabling Generalist Video Generation via A Multi-Agent Framework"
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📧 Please let us know if you find a mistake or have any suggestions by e-mail: lis221@lehigh.edu
)🚀️ Oct 9: Our Mora update v2 paper and training code will coming soon.
🚀️ Jun 13: Our code is released!
🚀️ Mar 20: Our paper "Mora: Enabling Generalist Video Generation via A Multi-Agent Framework" is released!
Mora is a multi-agent framework designed to facilitate generalist video generation tasks, leveraging a collaborative approach with multiple visual agents. It aims to replicate and extend the capabilities of OpenAI's Sora.
Inspired by OpenAI Sora: First Impressions, we utilize Mora to generate Shy kids video. Even though Mora has reached the similar level as Sora in terms of video duration, 80s, Mora still has a significant gap in terms of resolution, object consistency, motion smoothness, etc.
demo_shy_kids.mp4
- Multi-Agent Collaboration: Utilizes several advanced visual AI agents, each specializing in different aspects of the video generation process, to achieve high-quality outcomes across various tasks.
- Broad Spectrum of Tasks: Capable of performing text-to-video generation, text-conditional image-to-video generation, extending generated videos, video-to-video editing, connecting videos, and simulating digital worlds, thereby covering an extensive range of video generation applications.
- Open-Source and Extendable: Mora’s open-source nature fosters innovation and collaboration within the community, allowing for continuous improvement and customization.
- Proven Performance: Experimental results demonstrate Mora's ability to achieve performance that is close to that of Sora in various tasks, making it a compelling open-source alternative for the video generation domain.
| Input prompt | Output video |
|---|---|
| A vibrant coral reef teeming with life under the crystal-clear blue ocean, with colorful fish swimming among the coral, rays of sunlight filtering through the water, and a gentle current moving the sea plants. |  |
| A majestic mountain range covered in snow, with the peaks touching the clouds and a crystal-clear lake at its base, reflecting the mountains and the sky, creating a breathtaking natural mirror. |  |
| In the middle of a vast desert, a golden desert city appears on the horizon, its architecture a blend of ancient Egyptian and futuristic elements.The city is surrounded by a radiant energy barrier, while in the air, seve |  |
| Input prompt | Input image | Mora generated Video | Sora generated Video |
|---|---|---|---|
| Monster Illustration in the flat design style of a diverse family of monsters. The group includes a furry brown monster, a sleek black monster with antennas, a spotted green monster, and a tiny polka-dotted monster, all interacting in a playful environment. |  |
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| An image of a realistic cloud that spells “SORA”. |  |
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| Original video | Mora extended video | Sora extended video |
|---|---|---|
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| Instruction | Original video | Mora edited Video | Sora edited Video |
|---|---|---|---|
| Change the setting to the 1920s with an old school car. make sure to keep the red color. |  |
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| Put the video in space with a rainbow road | |
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| Input previous video | Input next video | Output connect Video |
|---|---|---|
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| Mora simulating video | Sora simulating video |
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Code will be released as soon as possible!
@article{yuan2024mora,
title={Mora: Enabling Generalist Video Generation via A Multi-Agent Framework},
author={Yuan, Zhengqing and Chen, Ruoxi and Li, Zhaoxu and Jia, Haolong and He, Lifang and Wang, Chi and Sun, Lichao},
journal={arXiv preprint arXiv:2403.13248},
year={2024}
}
@article{liu2024sora,
title={Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models},
author={Liu, Yixin and Zhang, Kai and Li, Yuan and Yan, Zhiling and Gao, Chujie and Chen, Ruoxi and Yuan, Zhengqing and Huang, Yue and Sun, Hanchi and Gao, Jianfeng and others},
journal={arXiv preprint arXiv:2402.17177},
year={2024}
}
@misc{openai2024sorareport,
title={Video generation models as world simulators},
author={OpenAI},
year={2024},
howpublished={https://openai.com/research/video-generation-models-as-world-simulators},
}