arXiv 2508.112842025-08-18cs.CV

TimeMachine：保持身份不变的细粒度面部年龄编辑

TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation

Yilin Mi, Qixin Yan, Zheng-Peng Duan, Chunle Guo, Hubery Yin, Hao Liu, Chen Li, Chongyi Li

AI总结：

本文提出基于扩散的 TimeMachine 框架，通过多交叉注意力注入年龄信息并结合潜在空间年龄分类器引导，实现保持身份不变的细粒度面部年龄编辑，并构建了百万级 HFFA 数据集。

摘要：

随着生成模型的进步，面部图像编辑已取得显著进展。然而，在保持个人身份的同时实现细粒度年龄编辑仍然是一项具有挑战性的任务。在本文中，我们提出了 TimeMachine，一种新颖的基于扩散的框架，能够在保持身份特征不变的同时实现准确的年龄编辑。为了实现细粒度年龄编辑，我们将高精度年龄信息注入多交叉注意力模块，该模块明确分离年龄相关特征和身份相关特征。这一设计有助于更准确地解耦年龄属性，从而允许对面部衰老进行精确且可控的操纵。此外，我们提出了一个年龄分类器引导（ACG）模块，该模块直接在潜在空间中预测年龄，而不是在训练期间执行去噪图像重建。通过采用轻量级模块引入年龄约束，这一设计在适度增加训练成本的情况下提高了年龄编辑的准确性。另外，为了解决缺乏大规模高质量面部年龄数据集的问题，我们构建了一个 HFFA 数据集（高质量细粒度面部年龄数据集），其中包含一百万张带有身份和面部属性标注的高分辨率图像。实验结果表明，TimeMachine 在细粒度年龄编辑方面达到了最先进的性能，同时保持了身份一致性。

英文摘要：

With the advancement of generative models, facial image editing has made significant progress. However, achieving fine-grained age editing while preserving personal identity remains a challenging task. In this paper, we propose TimeMachine, a novel diffusion-based framework that achieves accurate age editing while keeping identity features unchanged. To enable fine-grained age editing, we inject high-precision age information into the multi-cross attention module, which explicitly separates age-related and identity-related features. This design facilitates more accurate disentanglement of age attributes, thereby allowing precise and controllable manipulation of facial aging. Furthermore, we propose an Age Classifier Guidance (ACG) module that predicts age directly in the latent space, instead of performing denoising image reconstruction during training. By employing a lightweight module to incorporate age constraints, this design enhances age editing accuracy by modest increasing training cost. Additionally, to address the lack of large-scale, high-quality facial age datasets, we construct a HFFA dataset (High-quality Fine-grained Facial-Age dataset) which contains one million high-resolution images labeled with identity and facial attributes. Experimental results demonstrate that TimeMachine achieves state-of-the-art performance in fine-grained age editing while preserving identity consistency.

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