Video Generation Models as World Simulators

OpenAI's Sora: Video generation models as world simulators | Just Think AI
May 21, 2024

Video generation models as world simulators

Sora is trained on a diverse dataset of videos and images with variable durations, resolutions and aspect ratios. This allows the model to generate high fidelity video up to one minute in length depicting a wide range of real world scenes and events. While originally designed for video generation, models like Sora exhibit emergent capabilities for simulating aspects of the physical world like 3D spaces, object permanence, and basic physical interactions. For example, Sora can model consistent 3D spaces where objects and characters move realistically as the viewpoint shifts. It can also maintain the identity, state, and attributes of elements over time through occlusions. With continued scaling, such models could be used as customizable simulations of reality itself.

Training diffusion models on diverse videos and images

Sora is trained on a large, diverse dataset encompassing videos and images of varying durations, resolutions and aspect ratios. This allows the model to generate high fidelity video content depicting a wide range of real world scenes and events, with samples up to 60 seconds long. Training on such diverse data enables Sora to produce videos tailored to different use cases, devices, and formats.

Using as general purpose physical world simulators

While designed for video generation, Sora exhibits emergent capabilities for simulating various physical world attributes like 3D spaces, object permanence, and basic physical interactions between entities. For instance, Sora can model consistent 3D environments where characters and elements move realistically as the viewpoint shifts around a scene over time. The model can also maintain the identity, state, position, and other attributes of objects through occlusions and across shot transitions. With sufficient scale, such large video generation models could potentially simulate interactive worlds supporting customized scenarios spanning both physical and digital realities.

Turning visual data into patches

A video compression network encodes videos into a compact latent representation that is both temporally and spatially compressed. This latent representation is then divided into a sequence of spacetime patches which act as tokens for Sora's transformer architecture. The patch decomposition allows variable size images and videos to be represented a consistent way for efficient training while retaining visual information. At inference time, Sora can generate videos and images with flexible resolutions and aspect ratios by arranging randomly-initialized patches in an appropriately-sized grid.

Compressing videos into lower-dimensional latent space

A video compression network is trained to encode raw pixel videos into a latent representation with reduced spatial and temporal dimensionality. This compact encoding retains key visual information in an efficient form suitable for generative modeling, while discarding irrelevant noise and redundancy. Different preprocessing schemes and hyperparameters can be explored to balance compression rate, model capacity, and reconstruction fidelity.

Decomposing latent representation into spacetime patches

The compressed latent video representation is divided into a sequence of discrete spacetime elements termed patches. These patches essentially act as atomic visual tokens that serve as the basic computational units for Sora's underlying transformer architecture, analogous to how words are tokens for language models. The patch decomposition provides a unified way to represent variable duration videos and images for consistent and efficient generative modeling.

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Scaling transformers for video generation

Sora utilizes a diffusion transformer model that operates on spacetime patches. Example videos demonstrate Sora's sample quality visibly improving with increased model size and compute during training. This shows the potential for further gains in coherence and fidelity through continued scaling. Transformers have shown remarkable scaling across domains like language, computer vision, and image generation. This work finds diffusion transformers also scale effectively as video generation models.

Sora as a diffusion transformer model

Sora leverages a transformer-based generative framework known as a diffusion model. Specifically, it is trained to iteratively denoise and refine random input patches to predict high fidelity patches representing coherent video content. Transformers have useful architectural properties that translate well from language to video generation tasks in terms of modeling long-range dependencies and multi-modal context.

Improved sample quality with increased compute

Examples demonstrate Sora's video generation quality visibly improving over the course of training as model capacity and compute are increased. This empirically validates the potential for transformers to continue benefiting from greater scale in the video domain, gradually learning to model more complex visual dynamics just as language models have with text. The compute-sample quality correlation suggests grounds for optimism about future gains in video coherence and fidelity from further scaling advancements.

Variable durations, resolutions, aspect ratios

Training on data in its native form provides flexibility in sampling different resolutions and aspect ratios at inference time. For example, Sora can generate widescreen 1920x1080p videos, vertical 1080x1920 videos, and more. This lets content be created for different device formats. Empirically, training on variable aspect ratios also improves framing and composition. Videos generated by Sora have better positioning of subjects compared to relying on square crops during training.

Prompting with images and videos

Sora can generate video conditioned on images and video in addition to text prompts. For example, Sora can animate DALL-E generated still images by producing video continuations based on an image and text. Sora can also extend seed video segments forwards and backwards through time to create longer coherent samples. Techniques like SDEdit enable translating input video into different styles and environments guided by textual prompts as well.

Image generation capabilities

Sora can produce high fidelity static images through inference by arranging patches in a 2D grid instead of temporal sequence. This allows image generation up to 2048x2048 resolution using the same patch representation underlying video generation. The consistency enables seamless switching between sampling modalities.

Emerging simulation capabilities

As scale increases, Sora begins exhibiting agent-environment interaction behaviors that simulate aspects of physical reality. This includes demonstrating 3D consistency as the viewpoint shifts around a scene, maintaining identity and state of elements through long durations, modeling basic physical interactions, and simulating digital worlds like Minecraft gameplay. Current capabilities suggest that with further scaling, video generation models could simulate broader aspects of physical and digital realities.

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