DiffuseSlide: Training-Free High Frame Rate Video Generation Diffusion
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arXiv
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| Format: | Preprint |
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2025
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| author | Hwang, Geunmin Ko, Hyun-kyu Kim, Younghyun Lee, Seungryong Park, Eunbyung |
| author_facet | Hwang, Geunmin Ko, Hyun-kyu Kim, Younghyun Lee, Seungryong Park, Eunbyung |
| contents | Recent advancements in diffusion models have revolutionized video generation, enabling the creation of high-quality, temporally consistent videos. However, generating high frame-rate (FPS) videos remains a significant challenge due to issues such as flickering and degradation in long sequences, particularly in fast-motion scenarios. Existing methods often suffer from computational inefficiencies and limitations in maintaining video quality over extended frames. In this paper, we present a novel, training-free approach for high FPS video generation using pre-trained diffusion models. Our method, DiffuseSlide, introduces a new pipeline that leverages key frames from low FPS videos and applies innovative techniques, including noise re-injection and sliding window latent denoising, to achieve smooth, consistent video outputs without the need for additional fine-tuning. Through extensive experiments, we demonstrate that our approach significantly improves video quality, offering enhanced temporal coherence and spatial fidelity. The proposed method is not only computationally efficient but also adaptable to various video generation tasks, making it ideal for applications such as virtual reality, video games, and high-quality content creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DiffuseSlide: Training-Free High Frame Rate Video Generation Diffusion Hwang, Geunmin Ko, Hyun-kyu Kim, Younghyun Lee, Seungryong Park, Eunbyung Computer Vision and Pattern Recognition Recent advancements in diffusion models have revolutionized video generation, enabling the creation of high-quality, temporally consistent videos. However, generating high frame-rate (FPS) videos remains a significant challenge due to issues such as flickering and degradation in long sequences, particularly in fast-motion scenarios. Existing methods often suffer from computational inefficiencies and limitations in maintaining video quality over extended frames. In this paper, we present a novel, training-free approach for high FPS video generation using pre-trained diffusion models. Our method, DiffuseSlide, introduces a new pipeline that leverages key frames from low FPS videos and applies innovative techniques, including noise re-injection and sliding window latent denoising, to achieve smooth, consistent video outputs without the need for additional fine-tuning. Through extensive experiments, we demonstrate that our approach significantly improves video quality, offering enhanced temporal coherence and spatial fidelity. The proposed method is not only computationally efficient but also adaptable to various video generation tasks, making it ideal for applications such as virtual reality, video games, and high-quality content creation. |
| title | DiffuseSlide: Training-Free High Frame Rate Video Generation Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.01454 |