The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion
This work addresses the need for multi-dimensional and controllable facial aging visualization in applications like digital storytelling and health education, representing a novel method for a known bottleneck.
The paper tackles the problem of generating multiple plausible facial aging trajectories from a single image, conditioned on factors like environment and health, and achieves state-of-the-art performance in identity preservation, aging realism, and conditional alignment.
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.