World models have garnered significant attention as a promising research direction in artificial intelligence, yet a clear and unified definition remains lacking. In this paper, we introduce OpenWorldLib, a comprehensive and standardized inference framework for Advanced World Models. Drawing on the evolution of world models, we propose a clear definition: a world model is a model or framework centered on perception, equipped with interaction and long-term memory capabilities, for understanding and predicting the complex world. We further systematically categorize the essential capabilities of world models. Based on this definition, OpenWorldLib integrates models across different tasks within a unified framework, enabling efficient reuse and collaborative inference. Finally, we present additional reflections and analyses on potential future directions for world model research. Code link: https://github.com/OpenDCAI/OpenWorldLib
Figures & tables
Figure 1: Overview of our OpenWorldLib. Our OpenWorldLib establishes a unified framework for existing world model-related tasks, encompassing perception, understanding, memory, and generation of physical world inputs.
Figure 2: Illustration of our OpenWorldLib framework.
Figure 3: Demonstration of world model Implicit representation and explicit representation.
Figure 4: Demonstration of interactive video generation results.
Figure 5: Demonstration of 3D scene generation results.
Figure 6: Demonstration of simulator generation results.
School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA · School of Computing, University of Georgia, Athens, GA, USA · Tandon School of Engineering, New York University, Brooklyn, NY, USA +9