用单张照片生成不同年龄的逼真人脸，无需大量带年龄标签数据
AgeBooth: Controllable Facial Aging and Rejuvenation via Diffusion Models
- 通过提示词融合与矩阵融合技术实现无监督年龄控制
- 仅需单张参考图即可生成跨年龄人脸，视觉质量优于现有方法
- 适合需要可控人脸生成的影视、社交应用开发者
近期扩散模型研究聚焦于从参考图像生成身份一致的图像，但在保持身份一致性的同时精确控制年龄仍具挑战，且微调通常需昂贵的跨年龄配对数据。本文提出AgeBooth，一种新型特定年龄微调方法，可在不依赖大规模年龄标注数据集的情况下，有效提升基于适配器的身份个性化模型的年龄控制能力。为减少对大量年龄标注数据的依赖，我们利用衰老的线性特性，引入年龄条件提示词融合和基于SVDMix（矩阵融合技术）的年龄特定LoRA融合策略，实现高质量中间年龄人像生成。所提方法仅需一张参考图像，即可生成真实且身份一致的跨年龄人脸图像。实验表明，AgeBooth在年龄控制精度与图像质量方面均优于现有最先进编辑方法。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。