References
[1]. Goodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial networks [J]. Communications of the ACM, 2020, 63(11): 139-144.
[2]. Chan C., Ginosar S., Zhou T., et al. Everybody dance now [C]// Proceedings of the IEEE/CVF international conference on computer vision. 2019: 5933-5942.
[3]. Vondrick C., Pirsiavash H., Torralba A. Generating videos with scene dynamics [J]. Advances in neural information processing systems, 2016, 29.
[4]. Zhou Y., Berg T. L. Learning temporal transformations from time-lapse videos [C]// Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14. Springer International Publishing, 2016: 262-277.
[5]. Saito M., Matsumoto E., Saito S. Temporal generative adversarial nets with singular value clipping [C]// Proceedings of the IEEE international conference on computer vision. 2017: 2830-2839.
[6]. Tulyakov S., Liu M. Y., Yang X., et al. Mocogan: Decomposing motion and content for video generation [C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 1526-1535.
[7]. Dandi Y., Das A., Singhal S., et al. Jointly trained image and video generation using residual vectors [C]// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020: 3028-3042.
[8]. Unterthiner T., van Steenkiste S., Kurach K., et al. FVD: A new metric for video generation [J]. 2019.
[9]. Kimura S., Kawamoto K. Conditional mocogan for zero-shot video generation [J]. arXiv preprint arXiv:2109.05864, 2021.
[10]. Mirza M., Osindero S. Conditional generative adversarial nets [J]. arXiv preprint arXiv:1411.1784, 2014.
[11]. Wang Y., Bilinski P., Bremond F., et al. G3AN: Disentangling appearance and motion for video generation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 5264-5273.
[12]. Siarohin A., Lathuilière S., Tulyakov S., et al. Animating arbitrary objects via deep motion transfer [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 2377-2386.
Cite this article
Yang,Z. (2024). A review of motion generation technology. Applied and Computational Engineering,30,68-73.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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References
[1]. Goodfellow I., Pouget-Abadie J., Mirza M., et al. Generative adversarial networks [J]. Communications of the ACM, 2020, 63(11): 139-144.
[2]. Chan C., Ginosar S., Zhou T., et al. Everybody dance now [C]// Proceedings of the IEEE/CVF international conference on computer vision. 2019: 5933-5942.
[3]. Vondrick C., Pirsiavash H., Torralba A. Generating videos with scene dynamics [J]. Advances in neural information processing systems, 2016, 29.
[4]. Zhou Y., Berg T. L. Learning temporal transformations from time-lapse videos [C]// Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14. Springer International Publishing, 2016: 262-277.
[5]. Saito M., Matsumoto E., Saito S. Temporal generative adversarial nets with singular value clipping [C]// Proceedings of the IEEE international conference on computer vision. 2017: 2830-2839.
[6]. Tulyakov S., Liu M. Y., Yang X., et al. Mocogan: Decomposing motion and content for video generation [C]// Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 1526-1535.
[7]. Dandi Y., Das A., Singhal S., et al. Jointly trained image and video generation using residual vectors [C]// Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2020: 3028-3042.
[8]. Unterthiner T., van Steenkiste S., Kurach K., et al. FVD: A new metric for video generation [J]. 2019.
[9]. Kimura S., Kawamoto K. Conditional mocogan for zero-shot video generation [J]. arXiv preprint arXiv:2109.05864, 2021.
[10]. Mirza M., Osindero S. Conditional generative adversarial nets [J]. arXiv preprint arXiv:1411.1784, 2014.
[11]. Wang Y., Bilinski P., Bremond F., et al. G3AN: Disentangling appearance and motion for video generation [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 5264-5273.
[12]. Siarohin A., Lathuilière S., Tulyakov S., et al. Animating arbitrary objects via deep motion transfer [C]// Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019: 2377-2386.