Extended reality (XR) demands generative models that respond to users' tracked real-world motion, yet current video world models accept only coarse control signals such as text or keyboard input, limiting their utility for embodied interaction. We introduce a human-centric video world model that is conditioned on both tracked head pose and joint-level hand poses. For this purpose, we evaluate existing diffusion transformer conditioning strategies and propose an effective mechanism for 3D head and hand control, enabling dexterous hand--object interactions. We train a bidirectional video diffusion model teacher using this strategy and distill it into a causal, interactive system that generates egocentric virtual environments. We evaluate this generated reality system with human subjects and demonstrate improved task performance as well as a significantly higher level of perceived amount of control over the performed actions compared with relevant baselines.
@article{arxiv.2602.18422,
title = {Generated Reality: Human-centric World Simulation using Interactive Video Generation with Hand and Camera Control},
author = {Linxi Xie and Lisong C. Sun and Ashley Neall and Tong Wu and Shengqu Cai and Gordon Wetzstein},
journal= {arXiv preprint arXiv:2602.18422},
year = {2026}
}