The technical report (https://openai.com/research/video-generation-models-as-world-simulators) focuses on OpenAI’s research on large-scale training of generative models on video data. The researchers trained text-conditional diffusion models jointly on videos and images of variable durations, resolutions, and aspect ratios. They used a transformer architecture that operates on spacetime patches of video and image latent codes. The largest model, Sora, can generate a minute of high-fidelity video.
The report discusses the method for turning visual data of all types into a unified representation that enables large-scale training of generative models. It also includes a qualitative evaluation of Sora’s capabilities and limitations. The researchers take inspiration from large language models that acquire generalist capabilities by training on internet-scale data. They use visual patches as tokens, which have previously been shown to be an effective representation for models of visual data.
The researchers turn videos into patches by first compressing videos into a lower-dimensional latent space and then decomposing the representation into spacetime patches. They train a network that reduces the dimensionality of visual data and outputs a latent representation that is compressed both temporally and spatially. Sora is trained on and subsequently generates videos within this compressed latent space.
Sora is a diffusion model that takes noisy patches and conditioning information like text prompts as inputs. It is a diffusion transformer that scales effectively as video models. The researchers find that training on data at its native size provides several benefits, such as sampling flexibility and improved framing and composition.
The report also discusses the importance of language understanding in training text-to-video generation systems. The researchers apply the re-captioning technique introduced in DALL-E to videos and find that training on highly descriptive video captions improves text fidelity as well as the overall quality of videos.
The researchers also highlight the potential of Sora for a wide range of image and video editing tasks, such as creating perfectly looping video, animating static images, extending videos forwards or backwards in time, and editing videos from text prompts.
In summary, the report discusses OpenAI’s research on large-scale training of generative models on video data. The researchers use a transformer architecture that operates on spacetime patches of video and image latent codes. The largest model, Sora, can generate a minute of high-fidelity video. The report highlights the potential of video generation models as world simulators and their potential applications in various fields.