Abstract
In this paper, we propose a new way to generate videos via recurrent convolutional generative adversarial networks (CRGAN). The video tasks involving spatio-temporal series are more difficult than image tasks. In order to deal with spatio-temporal series tasks, we use a method that combines convolutional neural networks (CNN), which are used to deal with spatio relationships of videos, with Long Short Term Memory (LSTM), which is a variant of recurrent neural networks and used to deal with temporal relationships of videos, called convolutional recurrent neural networks (CRNN) to process the video inputs.Generative adversarial networks (GAN) is a method of unsupervised learning and has attained great improvements in image generation. In our paper, we combine CRNN with GAN and use unsupervised learning to generate videos. In the end, we will present some videos generated by our methods.
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Li, Y., Komma, T. (2019). Generating Videos Based on Convolutional Recurrent Generative Adversarial Networks. In: Cocchiarella, L. (eds) ICGG 2018 - Proceedings of the 18th International Conference on Geometry and Graphics. ICGG 2018. Advances in Intelligent Systems and Computing, vol 809. Springer, Cham. https://doi.org/10.1007/978-3-319-95588-9_116
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