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Creating video from text/image, generating loop video, extending video forward and backward

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Sora is an AI model developed by OpenAI that can create realistic and imaginative video scenes from text instructions. It's designed to simulate the physical world in motion, generating videos up to a minute long while maintaining visual quality and adhering to the user's prompt.

Sora is a diffusion model that starts with a video resembling static noise and gradually transforms it by removing the noise over many steps. It uses a transformer architecture, similar to GPT models, and represents videos and images as collections of smaller data units called patches.

Sora can generate a wide range of videos, including complex scenes with multiple characters, specific types of motion, and accurate details of subjects and backgrounds. It can also take an existing still image and animate it, or extend an existing video by filling in missing frames.

Sora may struggle with accurately simulating the physics of complex scenes, understanding specific instances of cause and effect, and maintaining spatial details over time. It can sometimes create physically implausible motion or mix up spatial details.

OpenAI is working with red teamers to adversarially test the model and is building tools to detect misleading content. They plan to include C2PA metadata in the future and are leveraging existing safety methods from their other products, such as text classifiers and image classifiers.

Sora is currently available to red teamers for assessing critical areas for harms or risks and to visual artists, designers, and filmmakers for feedback on how to advance the model for creative professionals.

If you're a creative professional, you can apply for access to Sora through OpenAI. Once granted access, you can use the model to generate videos based on your text prompts, enhancing your creative projects with unique and imaginative scenes.

Sora serves as a foundation for models that can understand and simulate the real world, which OpenAI believes is an important milestone towards achieving Artificial General Intelligence (AGI).

Sora has a deep understanding of language, enabling it to accurately interpret text prompts and generate compelling characters and scenes that express vibrant emotions. It can create multiple shots within a single video while maintaining consistent characters and visual style.

Sora uses a transformer architecture, similar to GPT models, and represents videos and images as collections of smaller units of data called patches. This unification of data representation allows the model to be trained on a wider range of visual data.

By giving the model foresight of many frames at a time, Sora can ensure that subjects remain consistent even when they go out of view temporarily.

Sora uses the recaptioning technique from DALL·E 3, which involves generating highly descriptive captions for the visual training data. This helps the model to follow the user's text instructions more faithfully in the generated videos.

OpenAI is planning to take several safety steps before integrating Sora into its products, including adversarial testing, developing detection classifiers, and leveraging existing safety methods from other products like DALL·E 3.

Sora can be used by filmmakers, animators, game developers, and other creative professionals to generate video content, storyboards, or even to prototype ideas quickly and efficiently.

OpenAI is actively engaging with policymakers, educators, and artists to understand concerns and identify positive use cases for the technology. They acknowledge that while they cannot predict all beneficial uses or abuses, learning from real-world use is critical for creating safer AI systems over time.

OpenAI has text classifiers that check and reject text input prompts violating usage policies, such as those requesting extreme violence, sexual content, hateful imagery, or unauthorized use of intellectual property.

A 'world model' in AI refers to a computational model that simulates the physical world and its dynamics, allowing the AI to understand and predict how objects and entities interact within it. In the context of Sora, this means the model has been trained to generate videos that not only follow textual prompts but also adhere to the physical laws and behaviors of the real world, such as gravity, motion, and object interactions. This capability is crucial for creating realistic and coherent video content from textual descriptions.