The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Video Frame Synthesis Using Deep Voxel Flow
Open accessAbstract
We address the problem of synthesizing new video frames in an existing video, either in-between existing frames (interpolation), or subsequent to them (extrapolation). This problem is challenging because video appearance and motion can be highly complex. Traditional optical-flow-based solutions often fail where flow estimation is challenging, while newer neural-network-based methods that hallucinate pixel values directly often produce blurry results. We combine the advantages of these two methods by training a deep network that learns to synthesize video frames by flowing pixel values from existing ones, which we call deep voxel flow. Our method requires no human supervision, and any video can be used as training data by dropping, and then learning to predict, existing frames. The technique is efficient, and can be applied at any video resolution. We demonstrate that our method produces results that both quantitatively and qualitatively improve upon the state-of-the-art.
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Scale-Recurrent Network for Deep Image Deblurring
Tao, X., Gao, H., Shen, X., Wang, J., & Jia, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00853 · Cited by 1,316
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
Zhou, H., Liu, Y., Liu, Z., Luo, P., & Wang, X. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33019299 · Cited by 416
Self-citationBurst Denoising with Kernel Prediction Networks
Mildenhall, B., Barron, J. T., Chen, J., Sharlet, D., Ng, R., & Carroll, R. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00265 · Cited by 404
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
Saito, M., Saito, S., Koyama, M., & Kobayashi, S. (2020) · International Journal of Computer Vision · doi:10.1007/s11263-020-01333-y · Cited by 116
Metadata
- Authors
- Liu, Z., Yeh, R. A., Tang, X., Liu, Y., & Agarwala, A.
- Venue
- 2017 IEEE International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 4473-4481
- OpenAlex
- W2586480386
- Cited by
- 743
- References
- 13
- FWCI
- 23.46
- Type
- conference paper
- Language
- en
- Open access
- green
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- Liu, Z., Yeh, R. A., Tang, X., Liu, Y., & Agarwala, A. (2017). Video Frame Synthesis Using Deep Voxel Flow. 2017 IEEE International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv.2017.478
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