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

TL;DR
TimeMachine is a diffusion-based framework that enables precise facial age editing while maintaining identity, utilizing high-precision age information and a novel age classifier guidance module.
Contribution
The paper introduces a new diffusion model with explicit age-identity disentanglement and an age classifier guidance module for improved age editing accuracy.
Findings
Achieves state-of-the-art age editing performance.
Successfully preserves identity during age manipulation.
Constructed a large-scale high-quality facial age dataset.
Abstract
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…
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Taxonomy
TopicsFace recognition and analysis
