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arXiv · 2510.05715

AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models

Shihao Zhu·Bohan Cao·Ziheng Ouyang·Zhen Li·Peng-Tao Jiang·Qibin Hou
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Abstract

Recent diffusion model research focuses on generating identity-consistent images from a reference photo, but they struggle to accurately control age while preserving identity, and fine-tuning such models often requires costly paired images across ages. In this paper, we propose AgeBooth, a novel age-specific finetuning approach that can effectively enhance the age control capability of adapterbased identity personalization models without the need for expensive age-varied datasets. To reduce dependence on a large amount of age-labeled data, we exploit the linear nature of aging by introducing age-conditioned prompt blending and an age-specific LoRA fusion strategy that leverages SVDMix, a matrix fusion technique. These techniques enable high-quality generation of intermediate-age portraits. Our AgeBooth produces realistic and identity-consistent face images across different ages from a single reference image. Experiments show that AgeBooth achieves superior age control and visual quality compared to previous state-of-the-art editing-based methods.

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Shihao Zhu, Bohan Cao, Ziheng Ouyang, Zhen Li, Peng-Tao Jiang, Qibin Hou. 2025-10-07. AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models. https://arxiv.org/abs/2510.05715

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