TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation
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arXiv
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| Format: | Preprint |
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2025
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| author | Mi, Yilin Yan, Qixin Duan, Zheng-Peng Guo, Chunle Yin, Hubery Liu, Hao Li, Chen Li, Chongyi |
| author_facet | Mi, Yilin Yan, Qixin Duan, Zheng-Peng Guo, Chunle Yin, Hubery Liu, Hao Li, Chen Li, Chongyi |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_11284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation Mi, Yilin Yan, Qixin Duan, Zheng-Peng Guo, Chunle Yin, Hubery Liu, Hao Li, Chen Li, Chongyi Computer Vision and Pattern Recognition 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. |
| title | TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.11284 |