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

TL;DR

The paper presents a training-free diffusion framework called Aging Multiverse that generates diverse, condition-aware facial aging trajectories from a single image, enabling realistic and controllable aging visualizations.

Contribution

It introduces a novel training-free diffusion method with attention mixing and simulated aging regularization for multi-trajectory facial aging.

Findings

State-of-the-art identity preservation and aging realism.

Effective condition control in aging simulations.

Superior performance over existing models in experiments.

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.