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

What is Sora

Sora is developed by OpenAIAI video generationModel, with the ability to convert text descriptions into videos, can create video scenes that are both realistic and imaginative. The model focuses on simulating motion in the physical world and is designed to help people solve problems that require real-world interaction. compared toPika、Runway、PixVerse、Morph Studio、GenmoWhile other AI video tools can only generate four or five seconds of video, Sora is able to generate videos of up to a minute while maintaining visual quality and a high degree of restoration of user input. In addition to creating videos from scratch, Sora can generate animations based on existing static images, or extend and complete existing videos.

It should be noted that although Sora’s functions appear to be very powerful, it is not yet officially open to the public. OpenAI is conducting red team testing, security checks and optimization on it. OpenAI’s official website currently only provides an introduction to Sora, a video demo, and technical explanations, and does not yet provide directly usable video generation tools or APIs.madewithsora.comVideos generated by Sora are collected on the website, and interested friends can go and watch them.

Sora’s main features

• Text-driven video generation: Sora can generate video content that matches the detailed text description provided by the user. These descriptions can involve scenes, characters, actions, emotions, etc.
• Video quality and fidelity: 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.
• Simulating the physical world: Sora is designed to simulate real-world motion and physical laws, making the generated videos more visually realistic and able to handle complex scenes and character actions.
• Multi-role and complex scene processing: The model is capable of handling video generation tasks involving multiple characters and complex backgrounds, although there may be limitations in some cases.
• Video expansion and completion: Sora can not only generate videos from scratch, but also animate them based on existing still images or video clips, or extend the length of existing videos.

Sora’s technical principles

• Text condition generation: The Sora model is able to generate videos based on text prompts by combining text information with video content. This ability allows the model to understand the user’s description and generate video clips that match it.
• Visual Patches: Sora decomposes videos and images into small visual chunks as low-dimensional representations of videos and images. This approach allows models to process and understand complex visual information while maintaining computational efficiency.
• video compression network: Before generating the video, Sora uses a video compression network to compress the raw video data into a low-dimensional latent space. This compression process reduces the complexity of the data, making 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 and temporal blocks as input to the model, enabling the model to process and understand the spatiotemporal characteristics of the video.
• Diffusion Model: Sora uses a diffusion model (based on Transformer architectureItmodel) as its core generation mechanism. Diffusion models generate content by gradually removing noise and predicting the original data. In video generation, this means that the model starts from a series of noise patches and gradually recovers clear video frames.
• Transformer architecture:Sora utilizes the Transformer architecture to handle space-time chunks. Transformer is a powerful neural network model that excels at processing sequence data such as text and time series. In Sora, Transformers are used to understand and generate sequences of video frames.
• large scale training: Sora is trained on large-scale video datasets, which enables the model to learn rich visual patterns and dynamic changes. Large-scale training helps improve the model’s generalization capabilities, enabling it to generate diverse and high-quality video content.
• Text to video generation: Sora converts textual prompts into detailed video descriptions by training a descriptive caption generator. These descriptions are then used to guide the video generation process, ensuring that the generated video content matches the text description.
• Zero-shot learning: Sora is able to perform specific tasks through zero-shot 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.
• Simulating the physical world: Sora has demonstrated the ability to simulate the physical world during the training process, such as 3D consistency and object persistence, indicating that the model can understand and simulate the physical laws in the real world to a certain extent.

Application scenarios of Sora

• Social media short film production: Content creators quickly create engaging short videos for sharing on social media platforms. Creators can easily turn their ideas into videos without investing a lot of time and resources into learning 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 messages in a short time. Sora can generate animations with strong visual impact, or simulate real scenes to demonstrate product features. In addition, Sora can also help companies test different advertising creatives 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 construction projects, allowing customers to more intuitively understand design intentions. Product designers can use Sora to demonstrate how a new product works or the user experience process.
• Film and television production: Assist directors and producers to quickly build storyboards or generate preliminary visual effects in pre-production. This helps the team better plan scenes and shots before actual filming. In addition, Sora can also be used to generate special effects previews, allowing production teams to explore different visual effects within 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 simulation videos of scientific experiments or reenactments of historical events, making the learning process more vivid and intuitive.

How to use Sora

OpenAI Sora currently does not provide public access. The model is being evaluated by the red team (security experts) and is only being tested and evaluated by a small number of visual artists, designers, and filmmakers. OpenAI has not specified a specific timetable for wider public availability, but it will likely be sometime in 2024. To gain access now, individuals need to qualify based on expert criteria defined by OpenAI, which includes belonging to relevant professional groups involved in assessing model usefulness and risk mitigation strategies.