Yong Guo

Robotic manipulation and scene prediction are unified in a single 3D-native world action model that directly reasons about metric space, achieving SOTA performance across diverse tasks.

Achieving state-of-the-art performance in real-world image super-resolution while maintaining a rapid single-step processing time and minimal computational overhead.

CG-World reveals that structured supervision from a vast array of world-state data can significantly improve action modeling and policy transfer in AI systems.