Human motion and activity provide a critical signal for understanding, predicting, and interacting with dynamic environments. In recent years, computer vision has made significant progress in human motion perception, activity understanding, and motion generation, providing a strong foundation for modeling human behavior and dynamics. Integrating these advances into visual world models that support prediction, planning, and decision-making is a key next step toward enabling embodied intelligent systems to reason and act effectively in human-populated scenes.
This workshop focuses on the challenge of integrating rich models of human motion and behavior into world models, an increasingly important yet still under-explored direction in visual world modeling.
Topics include: 1. modeling human motion and activity in complex, interactive scenes; 2. learning dynamic world models that incorporate human behavior to represent scenes, objects, and affordances; 3. enabling efficient and robust real-world deployment, including integrated perception-planning, safe navigation and autonomous driving, and improved generalization under noise, occlusions, and distribution shifts.
This topic is closely aligned with recent progress in visual world modeling and generative simulation which aim to capture vision-based representations of the scene structure, object relations, and human dynamics. The workshop will bring together research on human motion and activity modeling, dynamic scene understanding, and world models that explicitly account for human behavior as a central component of the environment. By uniting perspectives from computer vision, embodied AI, robotics, and graphics, the workshop provides a forum to explore human-centered world models that enable socially intelligent perception and action in applications such as dynamic scene understanding, predictive navigation, autonomous driving, and human-robot interaction.
