The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Video Frame Interpolation via Adaptive Convolution
Abstract
Video frame interpolation typically involves two steps: motion estimation and pixel synthesis. Such a two-step approach heavily depends on the quality of motion estimation. This paper presents a robust video frame interpolation method that combines these two steps into a single process. Specifically, our method considers pixel synthesis for the interpolated frame as local convolution over two input frames. The convolution kernel captures both the local motion between the input frames and the coefficients for pixel synthesis. Our method employs a deep fully convolutional neural network to estimate a spatially-adaptive convolution kernel for each pixel. This deep neural network can be directly trained end to end using widely available video data without any difficult-to-obtain ground-truth data like optical flow. Our experiments show that the formulation of video interpolation as a single convolution process allows our method to gracefully handle challenges like occlusion, blur, and abrupt brightness change and enables high-quality video frame interpolation.
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Cites
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ImageNet classification with deep convolutional neural networks
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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Cited by
Dynamic Neural Networks: A Survey
Han, Y., Huang, G., Song, S., Yang, L., Wang, H., & Wang, Y. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3117837 · Cited by 848
Burst Denoising with Kernel Prediction Networks
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A Review on Deep Learning Techniques for Video Prediction
Oprea, S., Martinez-Gonzalez, P., Garcia-Garcia, A., Castro-Vargas, J. A., Orts-Escolano, S., Garcia-Rodriguez, J., & Argyros, A. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3045007 · Cited by 301
Enhanced bi-prediction with convolutional neural network for high-efficiency video coding
Zhao, Z., Wang, S., Wang, S., Zhang, X., Ma, S., & Yang, J. (2019) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2018.2876399 · Cited by 75
Metadata
- Authors
- Niklaus, S., Mai, L., & Liu, F.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 2270-2279
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2604329646
- Cited by
- 499
- References
- 64
- FWCI
- 14.24
- Type
- conference paper
- Language
- en
- Open access
- closed
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