The goal of this workshop is to exchange ideas and establish communications among researchers working on building gener- alizable world models that describe how the physical world evolves in response to interacting agents (e.g. human and robots). Large-scale datasets of videos, images, and text hold the key for learning generalizable world models that are visually plau- sible. However, distilling useful physical information from such diverse unstructured data is challenging and requires careful attention to data curation, developing scalable algorithms, and implementing suitable training curricula. On the other hand, physics-based priors can enable learning plausible scene dynamics but it is difficult to scale to complex phenomenon that lack efficient solvers or even governing dynamic equations. Developing general world models that can simulate complex real-world phenomenon in a physically-plausible fashion can unlock enormous opportunities in generative modeling and robotics, and would be of wide interest to the larger AI community, and we believe this workshop falls at an ideal timing given recent signif- icant progress in both video-modeling models and physics-based simulation. This workshop aims to bring together researchers in machine learning, robotics, physics-based simulation, and computer vision broadly aspiring to build scalable world models by utilizing internet data, simulation, and beyond in myriad ways.