Time-adaptive Video Frame Interpolation based on Residual Diffusion

• URL: http://arxiv.org/abs/2504.05402v2
• Date: Tue, 15 Apr 2025 18:25:08 GMT
• Title: Time-adaptive Video Frame Interpolation based on Residual Diffusion
• Authors: Victor Fonte Chavez, Claudia Esteves, Jean-Bernard Hayet,
• Abstract summary: We propose a new diffusion-based method for video frame (VFI)<n>In this work, we propose a new diffusion-based method for video frame (VFI)<n>We provide extensive comparisons with respect to state-of-the-art models and show that our model outperforms these models on animation videos.
• Score: 2.5261465733373965
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we propose a new diffusion-based method for video frame interpolation (VFI), in the context of traditional hand-made animation. We introduce three main contributions: The first is that we explicitly handle the interpolation time in our model, which we also re-estimate during the training process, to cope with the particularly large variations observed in the animation domain, compared to natural videos; The second is that we adapt and generalize a diffusion scheme called ResShift recently proposed in the super-resolution community to VFI, which allows us to perform a very low number of diffusion steps (in the order of 10) to produce our estimates; The third is that we leverage the stochastic nature of the diffusion process to provide a pixel-wise estimate of the uncertainty on the interpolated frame, which could be useful to anticipate where the model may be wrong. We provide extensive comparisons with respect to state-of-the-art models and show that our model outperforms these models on animation videos. Our code is available at https://github.com/VicFonch/Multi-Input-Resshift-Diffusion-VFI.

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