The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918115952033792 |
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| author | Gong, Bang Qi, Luchao Wu, Jiaye Fu, Zhicheng Song, Chunbo Jacobs, David W. Nicholson, John Sengupta, Roni |
| author_facet | Gong, Bang Qi, Luchao Wu, Jiaye Fu, Zhicheng Song, Chunbo Jacobs, David W. Nicholson, John Sengupta, Roni |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion Gong, Bang Qi, Luchao Wu, Jiaye Fu, Zhicheng Song, Chunbo Jacobs, David W. Nicholson, John Sengupta, Roni Computer Vision and Pattern Recognition 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. |
| title | The Aging Multiverse: Generating Condition-Aware Facial Aging Tree via Training-Free Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.21008 |