Egocentric video generation with fine-grained control through body motion is a key requirement towards embodied AI agents that can simulate, predict, and plan actions. In this work, we propose EgoControl, a pose-controllable video diffusion model trained on egocentric data. We train a video prediction model to condition future frame generation on explicit 3D body pose sequences. To achieve precise motion control, we introduce a novel pose representation that captures both global camera dynamics and articulated body movements, and integrate it through a dedicated control mechanism within the diffusion process. Given a short sequence of observed frames and a sequence of target poses, EgoControl generates temporally coherent and visually realistic future frames that align with the provided pose control. Experimental results demonstrate that EgoControl produces high-quality, pose-consistent egocentric videos, paving the way toward controllable embodied video simulation and understanding.
@article{arxiv.2511.18173,
title = {EgoControl: Controllable Egocentric Video Generation via 3D Full-Body Poses},
author = {Enrico Pallotta and Sina Mokhtarzadeh Azar and Lars Doorenbos and Serdar Ozsoy and Umar Iqbal and Juergen Gall},
journal= {arXiv preprint arXiv:2511.18173},
year = {2025}
}