UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer

Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organizer (UFO), which is compatible with any diffusion-based video generation model. The UFO comprises a series of adaptive adapters with adjustable intensities, which can significantly enhance the consistency between the foreground and background of videos and improve image quality without altering the original model parameters when integrated. The training for UFO is simple, efficient, requires minimal resources, and supports stylized training. Its modular design allows for the combination of multiple UFOs, enabling the customization of personalized video generation models. Furthermore, the UFO also supports direct transferability across different models of the same specification without the need for specific retraining. The experimental results indicate that UFO effectively enhances video generation quality and demonstrates its superiority in public video generation benchmarks.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i5.32573
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
0.00
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article

UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer

D. Liu, Mingjiang Zhan, Fei Su, Zhicheng Zhao et al.
Proceedings of the AAAI Conference on Artificial Intelligence
Computer Graphics and Visualization Techniques
article

UFO: Enhancing Diffusion-Based Video Generation with a Uniform Frame Organizer

D. Liu, Mingjiang Zhan, Fei Su, Zhicheng Zhao, Zhaohui Hou, S.S. Han
article en

Abstract

Recently, diffusion-based video generation models have achieved significant success. However, existing models often suffer from issues like weak consistency and declining image quality over time. To overcome these challenges, inspired by aesthetic principles, we propose a non-invasive plug-in called Uniform Frame Organizer (UFO), which is compatible with any diffusion-based video generation model. The UFO comprises a series of adaptive adapters with adjustable intensities, which can significantly enhance the consistency between the foreground and background of videos and improve image quality without altering the original model parameters when integrated. The training for UFO is simple, efficient, requires minimal resources, and supports stylized training. Its modular design allows for the combination of multiple UFOs, enabling the customization of personalized video generation models. Furthermore, the UFO also supports direct transferability across different models of the same specification without the need for specific retraining. The experimental results indicate that UFO effectively enhances video generation quality and demonstrates its superiority in public video generation benchmarks.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(5)
Beijing University of Posts and Telecommunications (CN), Group Sense (China) (CN)
Openalex Percentile: Top 79%
Computer Graphics and Visualization Techniques
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