AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models

• URL: http://arxiv.org/abs/2510.05715v1
• Date: Tue, 07 Oct 2025 09:25:09 GMT
• Title: AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models
• Abstract summary: AgeBooth produces identity-consistent face images across different ages from a single reference image.<n> Experiments show that AgeBooth achieves superior age control and visual quality compared to previous state-of-the-art editing-based methods.
• Score: 39.98150173094697
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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