Benchmarking Video Frame Interpolation
Not on the map. Why?
Abstract (arXiv)
Video frame interpolation, the task of synthesizing new frames in between two or more given ones, is becoming an increasingly popular research target. However, the current evaluation of frame interpolation techniques is not ideal. Due to the plethora of test datasets available and inconsistent computation of error metrics, a coherent and fair comparison across papers is very challenging. Furthermore, new test sets have been proposed as part of method papers so they are unable to provide the in-depth evaluation of a dedicated benchmarking paper. Another severe downside is that these test sets violate the assumption of linearity when given two input frames, making it impossible to solve without an oracle. We hence strongly believe that the community would greatly benefit from a benchmarking paper, which is what we propose. Specifically, we present a benchmark which establishes consistent error metrics by utilizing a submission website that computes them, provides insights by analyzing the interpolation quality with respect to various per-pixel attributes such as the motion magnitude, contains a carefully designed test set adhering to the assumption of linearity by utilizing synthetic data, and evaluates the computational efficiency in a coherent manner.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 51 references that are in the map. First posted on arXiv 25 Mar 2024.
Builds on
34 of 51 references.
References · 34
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22 more
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Built on by
Citing papers per year: 2024: 2; 2025: 1.
3 papers.
- 2024VFIMamba: Video Frame Interpolation with State Space Modelscited by 22
- 2025Semantic Frame Interpolationcited by 1
- 2024Factorized Video Autoencoders for Efficient Generative Modellingcited by 0
- 2025Semantic Frame Interpolation7 Jul 2025
- 2024Factorized Video Autoencoders for Efficient Generative Modelling5 Dec 2024
- 2024VFIMamba: Video Frame Interpolation with State Space Models2 Jul 2024