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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.17128 |
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| _version_ | 1866910382795259904 |
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| author | Kiefhaber, Simon Niklaus, Simon Liu, Feng Schaub-Meyer, Simone |
| author_facet | Kiefhaber, Simon Niklaus, Simon Liu, Feng Schaub-Meyer, Simone |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_17128 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Benchmarking Video Frame Interpolation Kiefhaber, Simon Niklaus, Simon Liu, Feng Schaub-Meyer, Simone Computer Vision and Pattern Recognition 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. |
| title | Benchmarking Video Frame Interpolation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.17128 |