Add to ORKGOur project involves studying the usage of generative adversarial networks (GANs) to translate semantically segmented video to photo-realistic video in a process known as video-to-video synthesis. The model is able to learn a mapping from semantically segmented masks to real-life images which depict the corresponding semantic labels. To achieve this, we employ a conditional GAN-based learning method that produces output conditionally based on the source video to be translated. Our model is capable of synthesizing a translated video, given semantically labeled video, that resembles real video by accurately replicating low-frequency details from the source.Advisors(s): Dr. Mohammed AledhariTopic(s): Artificial IntelligenceCS 473
Analyzing video streams represents a huge problem not only in terms of accuracy and speed, but also...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Generating videos from text has proven to be a significant challenge for existing generative models....
Our project involves studying the usage of generative adversarial networks (GANs) to translate seman...
This electronic version was submitted by the student author. The certified thesis is available in th...
Generation of realistic high-resolution videos of human subjects is a challenging and important task...
With the advancements in deep learning models such as Convolutional Neural Networks (CNNs) and Gener...
Generative Adversarial Networks (GANs) have been witnessed tremendous successes in broad Computer Vi...
Given two video frames X0 and Xn+1, we aim to generate a series of intermediate frames Y1, Y2, . . ....
Conditional visual synthesis is the process of artificially generating images or videos that satisf...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative models have shown impressive results in generating synthetic images. However, video synth...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Video synthesis using deep learning methods is an important yet challenging task for the computer vi...
Learning the distribution of multi-object scenes with Generative Adversarial Networks (GAN) is chall...
Analyzing video streams represents a huge problem not only in terms of accuracy and speed, but also...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Generating videos from text has proven to be a significant challenge for existing generative models....
Our project involves studying the usage of generative adversarial networks (GANs) to translate seman...
This electronic version was submitted by the student author. The certified thesis is available in th...
Generation of realistic high-resolution videos of human subjects is a challenging and important task...
With the advancements in deep learning models such as Convolutional Neural Networks (CNNs) and Gener...
Generative Adversarial Networks (GANs) have been witnessed tremendous successes in broad Computer Vi...
Given two video frames X0 and Xn+1, we aim to generate a series of intermediate frames Y1, Y2, . . ....
Conditional visual synthesis is the process of artificially generating images or videos that satisf...
We live in a world made up of different objects, people, and environments interacting with each othe...
Generative models have shown impressive results in generating synthetic images. However, video synth...
Generative Adversarial Networks (GANs) have been extremely successful in various application domains...
Video synthesis using deep learning methods is an important yet challenging task for the computer vi...
Learning the distribution of multi-object scenes with Generative Adversarial Networks (GAN) is chall...
Analyzing video streams represents a huge problem not only in terms of accuracy and speed, but also...
Image-to-Image translation is a collection of computer vision problems that aim to learn a mapping b...
Generating videos from text has proven to be a significant challenge for existing generative models....