We introduce the Aging Multiverse, a framework for generating multiple plausible facial aging trajectories from a single image, each conditioned on external factors such as environment, health, and lifestyle. Unlike prior methods that model aging as a single deterministic path, our approach creates an aging tree that visualizes diverse futures. To enable this, we propose a training-free diffusion-based method that balances identity preservation, age accuracy, and condition control. Our key contributions include attention mixing to modulate editing strength and a Simulated Aging Regularization strategy to stabilize edits. Extensive experiments and user studies demonstrate state-of-the-art performance across identity preservation, aging realism, and conditional alignment, outperforming existing editing and age-progression models, which often fail to account for one or more of the editing criteria. By transforming aging into a multi-dimensional, controllable, and interpretable process, our approach opens up new creative and practical avenues in digital storytelling, health education, and personalized visualization.
@article{arxiv.2506.21008,
title = {The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion},
author = {Bang Gong and Luchao Qi and Jiaye Wu and Zhicheng Fu and Chunbo Song and David W. Jacobs and John Nicholson and Roni Sengupta},
journal= {arXiv preprint arXiv:2506.21008},
year = {2025}
}