In this paper, we investigate the possibility of leveraging the predictive power of machine learning to generate animated lightning bolts in the image space efficiently. To this end, we selected state-of-the-art machine learning architectures based on Generative Adversarial Network (GAN) and trained them on the commonly available videos. We demonstrate that visually convincing animations are achievable even when employing a limited dataset. The visual realism of the generated sequences of lightning bolts is assessed by conducting a user study on the participants
Learning to represent and generate videos from unlabeled data is a very challenging problem. To gene...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of ...
Background. Weather and weather effects are important features when trying to immerse the viewer int...
This article presents an interactive method for 3D cloud animation at the landscape scale by employi...
This electronic version was submitted by the student author. The certified thesis is available in th...
Given two video frames X0 and Xn+1, we aim to generate a series of intermediate frames Y1, Y2, . . ....
In this article, we present a machine learning-based method to locate lightning flashes using calcul...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
In this article, we present a machine learning-based method to locate lightning flashes using calcul...
Background. Previous studies in the area of video generation using generative adversarial networks h...
Photographic training can result in new photographs that, to human observers, appear to be at least ...
Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have...
Faculty advisor: Qi ZhaoThis research was supported by the Undergraduate Research Opportunities Prog...
This paper proposes two network architectures to perform video generation from captions using Variat...
Learning to represent and generate videos from unlabeled data is a very challenging problem. To gene...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of ...
Background. Weather and weather effects are important features when trying to immerse the viewer int...
This article presents an interactive method for 3D cloud animation at the landscape scale by employi...
This electronic version was submitted by the student author. The certified thesis is available in th...
Given two video frames X0 and Xn+1, we aim to generate a series of intermediate frames Y1, Y2, . . ....
In this article, we present a machine learning-based method to locate lightning flashes using calcul...
https://arxiv.org/abs/1701.05927 We provide a bridge between generative modeling in the Machine Lea...
In this article, we present a machine learning-based method to locate lightning flashes using calcul...
Background. Previous studies in the area of video generation using generative adversarial networks h...
Photographic training can result in new photographs that, to human observers, appear to be at least ...
Recent advancements in generative adversarial networks (GANs), using deep convolutional models, have...
Faculty advisor: Qi ZhaoThis research was supported by the Undergraduate Research Opportunities Prog...
This paper proposes two network architectures to perform video generation from captions using Variat...
Learning to represent and generate videos from unlabeled data is a very challenging problem. To gene...
Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic re...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of ...