The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion
Journal:
arXiv
Published Date:
Jun 26, 2025
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.
