Face Time Traveller : Travel Through Ages Without Losing Identity

TL;DR

A Face-Attribute-Aware Prompt Refinement strategy that encodes intrinsic and extrinsic aging cues for context-aware conditioning and an Adaptive Attention Control mechanism is introduced that dynamically balances cross-attention for semantic aging cues and self-attention for structural and identity preservation.

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.

Figures (25)

• Figure 1: Illustration of real-world use cases of face aging. Top: The Benjamin Button example from The Curious Case of Benjamin Button (2008), where Brad Pitt was digitally aged using a complex hybrid VFX pipeline danks2020not. Modern face aging models can achieve similar visual realism at significantly lower time and cost without prosthetics or manual VFX—while preserving the actor’s identity across different lifespans. Middle (left): Application in personalized character and gaming environments. Middle (right): Digital historical reconstruction. Bottom: Additional use cases.
• Figure 2: Illustration of cyclic identity similarity protocol. Input image (age 40) is first re-aged to the target age (60) and then reverted to its original age (40). Top row depicts the reference-based identity similarity between the re-aged and reference image, while bottom row shows the cyclic identity similarity between the input and reconstructed faces.
• Figure 3: Overview of the proposed FaceTT framework. Given an input face, the Face-Attribute-Aware Prompt Refinement strategy produces a refined, attribute-rich facial prompts. These prompts are encoded using the embedding network, where the source age embeddings guide the Angular Inversion process to obtain high-fidelity latent reconstructions. Both source and target age embeddings are then injected into the diffusion model through Adaptive Attention Control, enabling precise and identity-consistent age editing. Finally, the age-transformed face is generated.
• Figure 4: Illustration of the Angular Inversion technique with prior inversion techniques.
• Figure 5: Illustration of Adaptive Attention Control Mechanism.