HRVGAN: High Resolution Video Generation using Spatio-Temporal GAN

• Main Author: Sagar, Abhinav
• Format: Journal Article
• Language: English
• Published: 17-08-2020
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Computer Science - Learning
• DOI: 10.48550/arxiv.2008.09646

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 Fr\'echet Inception Distance as the evaluation
metrics, our network outperforms previous state of the art networks on
unsupervised video generation.