FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866910042189463552 |
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| author | Ding, Ganggui Chen, Hao Xu, Xiaogang |
| author_facet | Ding, Ganggui Chen, Hao Xu, Xiaogang |
| contents | Large pre-trained video diffusion models excel in video frame interpolation but struggle to generate high fidelity frames due to reliance on intrinsic generative priors, limiting detail preservation from start and end frames. Existing methods often depend on motion control for temporal consistency, yet dense optical flow is error-prone, and sparse points lack structural context. In this paper, we propose FC-VFI for faithful and consistent video frame interpolation, supporting \(4\times\)x and \(8\times\) interpolation, boosting frame rates from 30 FPS to 120 and 240 FPS at \(2560\times 1440\)resolution while preserving visual fidelity and motion consistency. We introduce a temporal modeling strategy on the latent sequences to inherit fidelity cues from start and end frames and leverage semantic matching lines for structure-aware motion guidance, improving motion consistency. Furthermore, we propose a temporal difference loss to mitigate temporal inconsistencies. Extensive experiments show FC-VFI achieves high performance and structural integrity across diverse scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_04899 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation Ding, Ganggui Chen, Hao Xu, Xiaogang Computer Vision and Pattern Recognition Large pre-trained video diffusion models excel in video frame interpolation but struggle to generate high fidelity frames due to reliance on intrinsic generative priors, limiting detail preservation from start and end frames. Existing methods often depend on motion control for temporal consistency, yet dense optical flow is error-prone, and sparse points lack structural context. In this paper, we propose FC-VFI for faithful and consistent video frame interpolation, supporting \(4\times\)x and \(8\times\) interpolation, boosting frame rates from 30 FPS to 120 and 240 FPS at \(2560\times 1440\)resolution while preserving visual fidelity and motion consistency. We introduce a temporal modeling strategy on the latent sequences to inherit fidelity cues from start and end frames and leverage semantic matching lines for structure-aware motion guidance, improving motion consistency. Furthermore, we propose a temporal difference loss to mitigate temporal inconsistencies. Extensive experiments show FC-VFI achieves high performance and structural integrity across diverse scenarios. |
| title | FC-VFI: Faithful and Consistent Video Frame Interpolation for High-FPS Slow Motion Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.04899 |