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arXiv · 2511.18173

EgoControl: Controllable Egocentric Video Generation via 3D Full-Body Poses

Enrico Pallotta·Sina Mokhtarzadeh Azar·Lars Doorenbos·Serdar Ozsoy·Umar Iqbal·Juergen Gall
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Abstract

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.

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Enrico Pallotta, Sina Mokhtarzadeh Azar, Lars Doorenbos, Serdar Ozsoy, Umar Iqbal, Juergen Gall. 2025-11-22. EgoControl: Controllable Egocentric Video Generation via 3D Full-Body Poses. https://arxiv.org/abs/2511.18173

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