MeVGAN: GAN-based Plugin Model for Video Generation with Applications in
  Colonoscopy
Łukasz Struski Tomasz Urbańczyk Krzysztof Bucki Bartłomiej Cupiał Aneta Kaczyńska Przemysław Spurek Jacek Tabor
Video generation is important, especially in medicine, as much data is given
in this form. However, video generation of high-resolution data is a very
demanding task for generative models, due to the large need for memory. In this
paper, we propose Memory Efficient Video GAN (MeVGAN) - a Generative
Adversarial Network (GAN) which uses plugin-type architecture. We use a
pre-trained 2D-image GAN and only add a simple neural network to construct
respective trajectories in the noise space, so that the trajectory forwarded
through the GAN model constructs a real-life video. We apply MeVGAN in the task
of generating colonoscopy videos. Colonoscopy is an important medical
procedure, especially beneficial in screening and managing colorectal cancer.
However, because colonoscopy is difficult and time-consuming to learn,
colonoscopy simulators are widely used in educating young colonoscopists. We
show that MeVGAN can produce good quality synthetic colonoscopy videos, which
can be potentially used in virtual simulators.