Face Time Traveller : Travel Through Ages Without Losing Identity

2026-02-26

AI Summary

This work proposes FaceTT, a diffusion-based framework addressing key challenges in facial age transformation—particularly identity preservation, background consistency, and fine-grained control—under large-scale aging scenarios where balancing identity retention and visual realism remains difficult. FaceTT integrates biological and environmental aging cues to enable context-aware age editing, and introduces three core components: Face-Attribute-Aware Prompt Refinement, tuning-free Angular Inversion, and an adaptive attention control mechanism. Extensive experiments demonstrate that FaceTT significantly outperforms current state-of-the-art methods across multiple benchmarks and in-the-wild datasets, achieving notable advances in identity consistency, background preservation, and aging authenticity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Search and Retrieval-Augmented AI: Agentic search

Abstract

Face aging, an ill-posed problem shaped by environmental and genetic factors, is vital in entertainment, forensics, and digital archiving, where realistic age transformations must preserve both identity and visual realism. However, existing works relying on numerical age representations overlook the interplay of biological and contextual cues. Despite progress in recent face aging models, they struggle with identity preservation in wide age transformations, also static attention and optimization-heavy inversion in diffusion limit adaptability, fine-grained control and background consistency. To address these challenges, we propose Face Time Traveller (FaceTT), a diffusion-based framework that achieves high-fidelity, identity-consistent age transformation. Here, we introduce a Face-Attribute-Aware Prompt Refinement strategy that encodes intrinsic (biological) and extrinsic (environmental) aging cues for context-aware conditioning. A tuning-free Angular Inversion method is proposed that efficiently maps real faces into the diffusion latent space for fast and accurate reconstruction. Moreover, an Adaptive Attention Control mechanism is introduced that dynamically balances cross-attention for semantic aging cues and self-attention for structural and identity preservation. Extensive experiments on benchmark datasets and in-the-wild testset demonstrate that FaceTT achieves superior identity retention, background preservation and aging realism over state-of-the-art (SOTA) methods.

Problem

• face aging
• identity preservation
• visual realism
• background consistency
• age transformation

Innovation

• diffusion model
• face aging
• identity preservation
• prompt refinement
• adaptive attention