We present GEM, a Generalizable Ego-vision Multimodal world model that predicts future frames using a reference frame, sparse features, human poses, and ego-trajectories. Hence, our model has precise control over object dynamics, ego-agent motion and human poses. GEM generates paired RGB and depth outputs for richer spatial understanding. We introduce autoregressive noise schedules to enable stable long-horizon generations. Our dataset is comprised of 4000+ hours of multimodal data across domains like autonomous driving, egocentric human activities, and drone flights. Pseudo-labels are used to get depth maps, ego-trajectories, and human poses. We use a comprehensive evaluation framework, including a new Control of Object Manipulation (COM) metric, to assess controllability. Experiments show GEM excels at generating diverse, controllable scenarios and temporal consistency over long generations. Code, models, and datasets are fully open-sourced.
@article{arxiv.2412.11198,
title = {GEM: A Generalizable Ego-Vision Multimodal World Model for Fine-Grained Ego-Motion, Object Dynamics, and Scene Composition Control},
author = {Mariam Hassan and Sebastian Stapf and Ahmad Rahimi and Pedro M B Rezende and Yasaman Haghighi and David Brüggemann and Isinsu Katircioglu and Lin Zhang and Xiaoran Chen and Suman Saha and Marco Cannici and Elie Aljalbout and Botao Ye and Xi Wang and Aram Davtyan and Mathieu Salzmann and Davide Scaramuzza and Marc Pollefeys and Paolo Favaro and Alexandre Alahi},
journal= {arXiv preprint arXiv:2412.11198},
year = {2024}
}