WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens
This work addresses the need for general world models in video generation, offering a novel approach that is not incremental but expands applicability beyond specific domains.
The paper tackles the problem of limited scenario-specific world models for video generation by introducing WorldDreamer, a general world model that predicts masked visual tokens to capture diverse world dynamics, achieving versatility in tasks like text-to-video conversion and video editing across natural scenes and driving environments.
World models play a crucial role in understanding and predicting the dynamics of the world, which is essential for video generation. However, existing world models are confined to specific scenarios such as gaming or driving, limiting their ability to capture the complexity of general world dynamic environments. Therefore, we introduce WorldDreamer, a pioneering world model to foster a comprehensive comprehension of general world physics and motions, which significantly enhances the capabilities of video generation. Drawing inspiration from the success of large language models, WorldDreamer frames world modeling as an unsupervised visual sequence modeling challenge. This is achieved by mapping visual inputs to discrete tokens and predicting the masked ones. During this process, we incorporate multi-modal prompts to facilitate interaction within the world model. Our experiments show that WorldDreamer excels in generating videos across different scenarios, including natural scenes and driving environments. WorldDreamer showcases versatility in executing tasks such as text-to-video conversion, image-tovideo synthesis, and video editing. These results underscore WorldDreamer's effectiveness in capturing dynamic elements within diverse general world environments.