Top 10 Text-to-Video Generative AI Models

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:

1. Lumiere (Google): A text-to-video diffusion model designed for synthesizing videos that portray realistic, diverse and coherent motion. You can also give Lumiere a still image and a text prompt to create a video based on the image.

2. Sora (OpenAI): A text-to-video model that can generate videos up to a minute long while maintaining visual quality and adherence to the user’s prompt. Sora is able to generate complex scenes with multiple characters, specific types of motion, and accurate details of the subject and background.

3. VideoPoet (Google): This model is capable of multitasking on a variety of video-centric inputs and outputs. The LLM can optionally take text as input to guide generation for text-to-video, image-to-video, stylization, and outpainting tasks.

4. Emu Video (Meta): A simple method for text to video generation based on diffusion models, factorizing the generation into two steps: First generating an image conditioned on a text prompt, then generating a video conditioned on the prompt and the generated image.

5. Imagen Video (Google): 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.

6. 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.

7. Make-A-Video (Meta): 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.

8. Phenaki (Google): 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.

9. Gen-2 (Runway): A multimodal AI system 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.

10. Pika: AI model that can generate short videos based on text descriptions you provide. It’s free to use and accessible through their Discord server. Pika allows you to refine the generated video in a few ways. You can specify changes to the scene, add sound effects, or even extend the video length.

Common features

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:

• They can generate videos from text descriptions only, or from text and image inputs.
• They can generate videos in various artistic styles, moods, with 3D object understanding.
• They can generate videos that are short (a few seconds) or long (several minutes).
• They can perform instruction-guided video editing, such background or subject of a video.
• They can use publicly available datasets or fine-tune on specific datasets.
• They can be accessed through various platforms, such as Hugging Face or Discord.