TGAN

TGAN is a type of generative adversarial network that is capable of learning representation from an unlabeled video dataset and producing a new video. The generator consists of two sub networks called a temporal generator and an image generator. Specifically, the temporal generator first yields a set of latent variables, each of which corresponds to a latent variable for the image generator. Then, the image generator transforms these latent variables into a video which has the same number of frames as the variables. The model comprised of the temporal and image generators can not only enable to efficiently capture the time series, but also be easily extended to frame interpolation. The authors opt for a WGAN as the basic GAN structure and objective, but use singular value clipping to enforce the Lipschitz constraint.