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).
This section needs to be updated.(February 2024) |
There are different models, including open source models. The demo version of CogVideo is an early text-to-video model "of 9.4 billion parameters", with their codes presented on GitHub. [4] Meta Platforms has a partial text-to-video [note 1] model called "Make-A-Video". [5] [6] [7] Google's Brain has released a research paper introducing Imagen Video, a text-to-video model with 3D U-Net. [8] [9] [10] [11] [12]
In March 2023, a landmark research paper by Alibaba was published, applying many of the principles found in latent image diffusion models to video generation. [13] [14] Services like Kaiber and Reemix have since adopted similar approaches to video generation in their respective products.
Matthias Niessner and Lourdes Agapito at AI company Synthesia work on developing 3D neural rendering techniques that can synthesise realistic video by using 2D and 3D neural representations of shape, appearances, and motion for controllable video synthesis of avatars. [15]
Alternative approaches to text-to-video models exist. [16]
Multiple high quality text-to-video models, AI systems that can generate video clips from prompted text, were released in 2022.
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).
This section needs to be updated.(February 2024) |
There are different models, including open source models. The demo version of CogVideo is an early text-to-video model "of 9.4 billion parameters", with their codes presented on GitHub. [4] Meta Platforms has a partial text-to-video [note 1] model called "Make-A-Video". [5] [6] [7] Google's Brain has released a research paper introducing Imagen Video, a text-to-video model with 3D U-Net. [8] [9] [10] [11] [12]
In March 2023, a landmark research paper by Alibaba was published, applying many of the principles found in latent image diffusion models to video generation. [13] [14] Services like Kaiber and Reemix have since adopted similar approaches to video generation in their respective products.
Matthias Niessner and Lourdes Agapito at AI company Synthesia work on developing 3D neural rendering techniques that can synthesise realistic video by using 2D and 3D neural representations of shape, appearances, and motion for controllable video synthesis of avatars. [15]
Alternative approaches to text-to-video models exist. [16]
Multiple high quality text-to-video models, AI systems that can generate video clips from prompted text, were released in 2022.