FLAVR: flow-free architecture for fast video frame interpolation
Author:
Funder
Directorate for Computer and Information Science and Engineering
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Computer Vision and Pattern Recognition,Hardware and Architecture,Software
Link
https://link.springer.com/content/pdf/10.1007/s00138-023-01433-y.pdf
Reference92 articles.
1. Meyer, S., Djelouah, A., McWilliams, B., Sorkine-Hornung, A., Gross, M., Schroers, C.: Phasenet for video frame interpolation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 498–507 (2018)
2. Bao, W., Lai, W.-S., Ma, C., Zhang, X., Gao, Z., Yang, M.-H.: Depth-aware video frame interpolation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3703–3712 (2019)
3. Xue, T., Chen, B., Wu, J., Wei, D., Freeman, W.T.: Video enhancement with task-oriented flow. Int. J. Comput. Vision 127(8), 1106–1125 (2019)
4. Lee, H., Kim, T., Chung, T.-y., Pak, D., Ban, Y., Lee, S.: Adacof: Adaptive collaboration of flows for video frame interpolation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5316–5325 (2020)
5. Jiang, H., Sun, D., Jampani, V., Yang, M.-H., Learned-Miller, E., Kautz, J.: Super slomo: High quality estimation of multiple intermediate frames for video interpolation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9000–9008 (2018)
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