Filtered to video world models · clear filter
This paper surveys the development of video world models that capture the underlying dynamics of the world depicted in videos, and how they relate to video foundation models. Practitioners in AI and machine learning might care about this research because it aims to create more realistic and interactive video models that can be used in applications such as virtual reality, robotics, and autonomous systems.
This paper introduces SolarWM, an open framework for training video world models from diverse datasets and using different video backbones, allowing for more consistent and reproducible results. Practitioners can use SolarWM to build interactive video world models that can be applied to various real-world applications.
This paper introduces WorldCrafter, a video world model that learns to remember past observations and use them to generate consistent and accurate video representations, even when exploring dynamic environments from different viewpoints. Practitioners might care about this work because it can improve the reliability and effectiveness of interactive exploration applications, such as virtual reality or autonomous robots.
This paper develops a new AI model, Hy-Nomas, that can generate interactive worlds in response to user commands, allowing for more realistic and dynamic environments. Practitioners in fields like game development or virtual reality might care about this work because it could enable more immersive and interactive experiences.
This paper introduces a new AI model called DreamX-Phi 1.0 that can predict what will happen in a robotic manipulation scenario, given an initial state and instructions. This model is useful for robotics developers because it can help them design more reliable and efficient robotic systems.