Unified Arbitrary-Time Video Frame Interpolation and Prediction
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916642498281472 |
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| author | Jin, Xin Wu, Longhai Chen, Jie Cho, Ilhyun Hahm, Cheul-Hee |
| author_facet | Jin, Xin Wu, Longhai Chen, Jie Cho, Ilhyun Hahm, Cheul-Hee |
| contents | Video frame interpolation and prediction aim to synthesize frames in-between and subsequent to existing frames, respectively. Despite being closely-related, these two tasks are traditionally studied with different model architectures, or same architecture but individually trained weights. Furthermore, while arbitrary-time interpolation has been extensively studied, the value of arbitrary-time prediction has been largely overlooked. In this work, we present uniVIP - unified arbitrary-time Video Interpolation and Prediction. Technically, we firstly extend an interpolation-only network for arbitrary-time interpolation and prediction, with a special input channel for task (interpolation or prediction) encoding. Then, we show how to train a unified model on common triplet frames. Our uniVIP provides competitive results for video interpolation, and outperforms existing state-of-the-arts for video prediction. Codes will be available at: https://github.com/srcn-ivl/uniVIP |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_02316 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Unified Arbitrary-Time Video Frame Interpolation and Prediction Jin, Xin Wu, Longhai Chen, Jie Cho, Ilhyun Hahm, Cheul-Hee Computer Vision and Pattern Recognition Video frame interpolation and prediction aim to synthesize frames in-between and subsequent to existing frames, respectively. Despite being closely-related, these two tasks are traditionally studied with different model architectures, or same architecture but individually trained weights. Furthermore, while arbitrary-time interpolation has been extensively studied, the value of arbitrary-time prediction has been largely overlooked. In this work, we present uniVIP - unified arbitrary-time Video Interpolation and Prediction. Technically, we firstly extend an interpolation-only network for arbitrary-time interpolation and prediction, with a special input channel for task (interpolation or prediction) encoding. Then, we show how to train a unified model on common triplet frames. Our uniVIP provides competitive results for video interpolation, and outperforms existing state-of-the-arts for video prediction. Codes will be available at: https://github.com/srcn-ivl/uniVIP |
| title | Unified Arbitrary-Time Video Frame Interpolation and Prediction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.02316 |