Text-to-video model
Machine learning model / From Wikipedia, the free encyclopedia
A text-to-video model is a machine learning model which takes a natural language description as input and producing a video or multiples videos from the input.[1]
Video prediction on making objects realistic in a stable background is performed by using recurrent neural network for a sequence to sequence model with a connector convolutional neural network encoding and decoding each frame pixel by pixel,[2] creating video using deep learning.[3] Testing of the data set in conditional generative model for existing information from text can be done by variational autoencoder and generative adversarial network (GAN).