How to Build World Models|ECCV'26

About

World models are frameworks that learn the internal representation of the world and are able to predict its future states. World modeling is not a new concept, however, in recent years due to significant improvement in computational power and abundance of available data, building effective world models is becoming a reality. World models are universally regarded as the next frontier in AI and a path to achieving human-level intelligence.

This workshop brings researchers from academia and industry working at the intersection of computer vision, robotics, language, and generative modeling to explore what distinguishes world models from other predictive systems, such as video generation models, whether existing world models are sufficient for embodied AI, which modalities a good world model should posses, what data and learning schemas are best suited for training world models, and how to effectively integrate world models within embodied AI.

Topics of interest

World models integration into embodied AI systems
Data generation, training, and optimization
Simulation, imagination, and generative latent models
Physical and spatial representation learning

Evaluation protocols and benchmarking
Learned world models for planning and control
Scaling world models across different domains
Sim-to-real transfer for real-world deployment

Schedule

TBA

Speakers

Call for Papers

We invite original research contributions on world modeling and embodied AI, aligned with any of the topics of interest of the workshop. We accept both full-length papers (archival), limited to 14 pages (excluding references) and short papers (non-archival), limited to 7 pages (excluding references). The submitted works should follow the official ECCV'26 guidelines. The submissions are evaluated based on the novelty, relevance to the workshop topics, and significance. The accepted full-length papers will be included in ECCV workshop proceedings.