Sora – OpenAI launches AI text-to-video generation model | AI toolset

What is Sora

Sora was developed by OpenAIAI video generationModels, with the ability to convert text descriptions into videos, can create video scenes that are both realistic and imaginative. The model focuses on simulating the movement of the physical world and aims to help people solve problems that require real-world interactions. Compared withPika、Runway、PixVerse、Morph Studio、GenmoWith only four or five seconds of AI video tool, Sora can generate videos up to one minute while maintaining visual quality and high restoration of user input. In addition to creating videos from scratch, Sora can also generate animations based on existing still images, or expand and complete existing videos.

It should be noted that although Sora’s functions seem very powerful, they have not been officially opened to the public yet, and OpenAI is conducting red team testing, security checks and optimizations. Currently, there are only introductions, video demos and technical explanations of Sora on the official website of OpenAI, and no video generation tools or APIs can be provided that can be used directly.madewithsora.comThe website collects videos generated by Sora, and interested friends can go and watch them.

The main functions of Sora

• Text-driven video generation:Sora is able to generate video content that matches it based on the detailed text description provided by the user. These descriptions can involve scenes, characters, actions, emotions, and so on.
• Video quality and loyalty: The generated video maintains high-quality visual effects and closely follows the user’s text prompts to ensure that the video content matches the description.
• Simulate the physical world:Sora aims to simulate the motion and physical laws of the real world, making the generated video more visually realistic and able to handle complex scenes and character actions.
• Multi-character and complex scene processing: The model is able to handle video generation tasks that contain multiple characters and complex backgrounds, although in some cases there may be limitations.
• Video expansion and completion: Sora can not only generate videos from scratch, but also animation based on existing still images or video clips, or extend the length of existing videos.

The technical principles of Sora

OpenAI Sora’s technical architecture conjecture

• Text condition generation:The Sora model is able to generate videos based on text prompts, which is achieved by combining text information with video content. This capability allows the model to understand the user’s description and generate video clips that match it.
• Visual patches: Sora breaks down video and images into small blocks of visual blocks as low-dimensional representations of video and images. This approach allows the model to process and understand complex visual information while maintaining computational efficiency.
• Video compression network: Before generating video, Sora uses a video compression network to compress raw video data into a low-dimensional potential space. This compression process reduces the complexity of the data and makes it easier for the model to learn and generate video content.
• Spacetime patches: After video compression, Sora further decomposes the video representation into a series of spatial time blocks as input to the model, so that the model can process and understand the spatiotemporal characteristics of the video.
• Diffusion Model: Sora adopts diffusion model (based on Transformer architectureItmodel) as its core generation mechanism. The diffusion model generates content by gradually removing noise and predicting the original data. In video generation, this means that the model will gradually restore clear video frames from a series of noise patches.
• Transformer architecture:Sora uses the Transformer architecture to handle spatial time blocks. Transformer is a powerful neural network model that excels in processing sequence data such as text and time series. In Sora, Transformer is used to understand and generate video frame sequences.
• Large-scale training:Sora trains on large-scale video datasets, which enables the model to learn rich visual patterns and dynamic changes. Large-scale training helps improve the generalization ability of the model, enabling it to generate diverse and high-quality video content.
• Text to video generation: Sora converts text prompts into detailed video descriptions by training a descriptive subtitle generator. These descriptions are then used to guide the video generation process, ensuring that the generated video content matches the text description.
• Zero sample learning:Sora is able to perform specific tasks through zero sample learning, such as simulating a specific style of video or game. That is, the model can generate corresponding video content based on text prompts without direct training data.
• Simulate the physical world: Sora demonstrated the ability to simulate the physical world during training, such as 3D consistency and object durability, indicating that the model can understand and simulate physical laws in the real world to a certain extent.

Sora application scenarios

• Social media short film production: Content creators quickly create attractive short videos for sharing on social media platforms. Creators can easily convert their ideas into video without investing a lot of time and resources to learn video editing software. Sora can also generate video content suitable for specific formats and styles based on the characteristics of social media platforms (such as short videos, live broadcasts, etc.).
• Advertising and marketing:Quickly generate advertising videos to help brands convey core information in a short period of time. Sora can generate animations with strong visual impact, or simulate real scenes to show product features. In addition, Sora can help businesses test different advertising ideas and find the most effective marketing strategies through rapid iteration.
• Prototyping and concept visualization: For designers and engineers, Sora can serve as a powerful tool to visualize their designs and concepts. For example, architects can use Sora to generate three-dimensional animations of architectural projects, allowing customers to understand design intentions more intuitively. Product designers can use Sora to demonstrate how new products work or user experience processes.
• Film and television production: Assist directors and producers to quickly build storyboards in pre-production, or generate initial visual effects. This can help the team better plan the scene and shots before the actual shots. In addition, Sora can also be used to generate special effects previews, allowing production teams to explore different visual effects on a limited budget.
• Education and training:Sora can be used to create educational videos that help students better understand complex concepts. For example, it can generate simulated videos of scientific experiments, or reproduce historical events, making the learning process more vivid and intuitive.

How to use Sora

OpenAI Sora currently does not provide an access to public access, and the model is being evaluated by the Red Team (security experts) and is only tested and evaluated by a few visual artists, designers and filmmakers. OpenAI does not specify a specific timetable for wider public availability, but it may be sometime in 2024. To gain access now, individuals need to be eligible according to the expert criteria defined by OpenAI, including relevant professional groups involved in assessing the usefulness of the model and risk mitigation strategies.