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arXiv · 2506.21008

The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion

Bang Gong·Luchao Qi·Jiaye Wu·Zhicheng Fu·Chunbo Song·David W. Jacobs·John Nicholson·Roni Sengupta
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Abstract

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.

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Bang Gong, Luchao Qi, Jiaye Wu, Zhicheng Fu, Chunbo Song, David W. Jacobs, John Nicholson, Roni Sengupta. 2025-06-26. The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion. https://arxiv.org/abs/2506.21008

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