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World models play a crucial role in understanding and predicting the dynamicsof the world, which is essential for video generation. However, existing worldmodels are confined to specific scenarios such as gaming or driving, limitingtheir ability to capture the complexity of general world dynamic environments.Therefore, we introduce WorldDreamer, a pioneering world model to foster acomprehensive comprehension of general world physics and motions, whichsignificantly enhances the capabilities of video generation. Drawinginspiration from the success of large language models, WorldDreamer framesworld modeling as an unsupervised visual sequence modeling challenge. This isachieved by mapping visual inputs to discrete tokens and predicting the maskedones. During this process, we incorporate multi-modal prompts to facilitateinteraction within the world model. Our experiments show that WorldDreamerexcels in generating videos across different scenarios, including naturalscenes and driving environments. WorldDreamer showcases versatility inexecuting tasks such as text-to-video conversion, image-tovideo synthesis, andvideo editing. These results underscore WorldDreamer's effectiveness incapturing dynamic elements within diverse general world environments.

WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens