Softmax Splatting for Video Frame Interpolation
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Cited by 27 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Differentiable image sampling in the form of backward warping has seen broad adoption in tasks like depth estimation and optical flow prediction. In contrast, how to perform forward warping has seen less attention, partly due to additional challenges such as resolving the conflict of mapping multiple pixels to the same target location in a differentiable way. We propose softmax splatting to address this paradigm shift and show its effectiveness on the application of frame interpolation. Specifically, given two input frames, we forward-warp the frames and their feature pyramid representations based on an optical flow estimate using softmax splatting. In doing so, the softmax splatting seamlessly handles cases where multiple source pixels map to the same target location. We then use a synthesis network to predict the interpolation result from the warped representations. Our softmax splatting allows us to not only interpolate frames at an arbitrary time but also to fine tune the feature pyramid and the optical flow. We show that our synthesis approach, empowered by softmax splatting, achieves new state-of-the-art results for video frame interpolation.
Key facts. Cited by 276 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Mar 2020.
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Citing papers per year: 2021: 41; 2022: 60; 2023: 45; 2024: 60; 2025: 41; 2026: 29.
276 papers.
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- 2026ZoomDiff: A High-Fidelity Diffusion Model for Dual-Camera Smooth Zooming23 Sep 2026
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- 2026SPEED: One-Step Pixel Diffusion for High-quality Video Frame Interpolation17 Jul 2026
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- 2026GenStream: Semantic Streaming Framework for Generative Reconstruction of Human-centric Media16 Sep 2026
- 2026WiFlow: Estimating Optical Flow using WiFi Channel State Information2 Sep 2026
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