Real-Time Face Transition using Deepfake Technology (Gan Model)

Many machine learning models require huge datasets to get trained, to make correct predictions, or to increase their accuracy, that's where generative adversarial network (GAN) comes into the picture. GAN is a deep learning model, which can be used to create artificial data. It consists of two neural networks that cooperate in a way that resembles a game: a generator network and a discriminator network. Within the suggested model in this work, a dense motion network-based first-order motion model for image animation is developed. Here a trained GAN extracts the face landmarks from the driving video and develops the embedding model to create the synthesis video using the dedicated module to prepare the Deepfakes, employing key point detectors as a baseline. Lastly, an approach that makes use of dense motion networks to increase the effectiveness of a collection of GAN generators is provided. With the help of the sequel driving combination of driving video with the source image, the given results produce the augmented animation video. Hence, it is tried to implement a lighter model which consists of modules, with the approximate same accuracy when compared with other implementations of GAN. This work has a wide range of applications, including doubling dataset counts with a small number of sources, creating real-time backgrounds and characters for the gaming and animation industries using CG platforms, translating clothes, predicting videos, creating 3D objects, etc.