AI text-to-video models are machine learning models that can generate videos from natural language descriptions. They use different techniques to understand the meaning and context of the input text and then create a sequence of images that are both spatially and temporally consistent with the text.
Depending on the model, the video generation can be influenced by additional inputs such as images or video clips, or by instructions such as style, mood, or content. Some of the models can also perform video editing or synthesis tasks, such as changing the background, foreground, or subject of a video.
Some of the most recent and advanced text-to-video models are:
Imagen Video Creator: A text-to-video version of Google’s Imagen generative model, which can produce high-quality and diverse videos from text prompts. It uses a transformer-based architecture and a diffusion-based decoder to generate videos in a coarse-to-fine manner.
CogVideo: A text-to-video model that can generate videos with controllable attributes, such as style, viewpoint, and motion. It uses a conditional variational autoencoder (CVAE) and a recurrent neural network (RNN) to encode the text and image features, and a convolutional LSTM to decode the video frames.
Make-A-Video: A text-to-video model that can generate videos with realistic and coherent scenes, objects, and actions. It uses a scene graph parser to extract the semantic structure of the text, and a graph neural network to generate a video layout. It then uses a GAN-based renderer to synthesize the video frames from the layout.
Phenaki: A text-to-video model that can generate videos with natural phenomena, such as fire, smoke, and water. It uses a physics-based simulator to model the dynamics of the phenomena, and a neural network to render the video frames from the simulation.
Runway Gen-2: A multimodal AI system, developed by Runway Research, that can generate novel videos with text, images or video clips. Gen-2 can also transfer the style of any image or prompt to every frame of a video, turn mockups into fully stylized and animated renders, isolate subjects in a video and modify them with simple text prompts, and turn untextured renders into realistic outputs.
Text2Video-Zero: A multimodal AI system that can generate videos from text descriptions without any training or optimization. It leverages the power of existing text-to-image synthesis methods, such as Stable Diffusion, and modifies them to produce realistic and consistent videos that match the text. It can also generate videos from text and image inputs, such as poses or edges, or perform instruction-guided video editing.
NUWA: A series of cutting-edge multimodal generative models developed by Microsoft Research that can produce or manipulate images and videos. NUWA-Infinity can generate arbitrarily-sized long-duration videos, NUWA-XL is directly trained on long films and can produce extremely long videos.
AI models learn to generate video from text by using various techniques such as deep learning, recurrent neural networks, transformers, diffusion models, and GANs. These techniques help the models to understand the context and semantics of the text input and generate corresponding video frames that are realistic and coherent.
Some of the common features of text-to-video AI models are: