Video generation method combining variational auto-encoder and generative adversarial network

Abstract

The invention discloses a video generation method combining a variational auto-encoder and a generative adversarial network. Belonging to the technical field of video generation, the method comprisesthe following steps: a generator of the generative adversarial network does not directly generate a video but generate a series of associated hidden variables, the hidden variables pass through atrained decoder of the variational auto-encoder to generate a series of related images, a discriminator of the generative adversarial network does not directly discriminate the video, but enables the videoto pass through the encoder of the variational auto-encoder to obtain a series of low-dimensional hidden variables and discriminate the hidden variables. According to the method, the video can be generated according to the input description text; the method is advantaged in that a problem of poor inter-frame continuity in video generation is solved, inter-frame continuity of video generation is improved, the training step is divided into two parts of training the variation auto-encoder and training the generative adversarial network based on the trained variation auto-encoder, and training iseasier and more stable.

Description

technical field [0001] The invention belongs to the technical field of video generation, and in particular relates to a video generation method combining a variational autoencoder and a generative confrontation network. Background technique [0002] In recent years, with the wide application of artificial intelligence technology in various industries, the productivity of all walks of life has been greatly improved. For example, in the production of TV programs, video generation technology can greatly reduce human work. In the industry, companies such as NVIDIA have proposed video generation technology based on generative adversarial networks to meet video generation needs in various situations. However, the existing video generation methods often have problems such as insufficient temporal continuity between frames in the generated video and image deformation when the input information is insufficient, thereby reducing the quality of video generation. [0003] Diederik P Ki...

AI Technical Summary

Problems solved by technology

However, the existing video generation methods often have problems such as insufficient time continuity between frames in the generated video and image deformation when the input information is insufficient, thereby reducing the quality of video generation

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