DeepAI AI Chat
Log In Sign Up

HRVGAN: High Resolution Video Generation using Spatio-Temporal GAN

by   abhinavsagar, et al.

In this paper, we present a novel network for high resolution video generation. Our network uses ideas from Wasserstein GANs by enforcing k-Lipschitz constraint on the loss term and Conditional GANs using class labels for training and testing. We present Generator and Discriminator network layerwise details along with the combined network architecture, optimization details and algorithm used in this work. Our network uses a combination of two loss terms: mean square pixel loss and an adversarial loss. The datasets used for training and testing our network are UCF101, Golf and Aeroplane Datasets. Using Inception Score and Fréchet Inception Distance as the evaluation metrics, our network outperforms previous state of the art networks on unsupervised video generation.


Virtual Adversarial Lipschitz Regularization

Generative adversarial networks (GANs) are one of the most popular appro...

Towards High Resolution Video Generation with Progressive Growing of Sliced Wasserstein GANs

The extension of image generation to video generation turns out to be a ...

Multi-Variate Temporal GAN for Large Scale Video Generation

In this paper, we present a network architecture for video generation th...

On the regularization of Wasserstein GANs

Since their invention, generative adversarial networks (GANs) have becom...

Spider GAN: Leveraging Friendly Neighbors to Accelerate GAN Training

Training Generative adversarial networks (GANs) stably is a challenging ...

Temporally Coherent GANs for Video Super-Resolution (TecoGAN)

Adversarial training has been highly successful in the context of image ...

Are conditional GANs explicitly conditional?

This paper proposes two important contributions for conditional Generati...