TimeMachine: Fine-Grained Facial Age Editing with Identity Preservation

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 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.