MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration
- URL: http://arxiv.org/abs/2603.18645v1
- Date: Thu, 19 Mar 2026 09:11:07 GMT
- Title: MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration
- Abstract summary: MeInTime is a diffusion-based face restoration method that extends reference-based restoration from same-age to cross-age settings.<n>Given one or few reference images along with an age prompt corresponding to the degraded input, MeInTime achieves faithful restoration with both identity fidelity and age consistency.
- Score: 24.354251784441217
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: To better preserve an individual's identity, face restoration has evolved from reference-free to reference-based approaches, which leverage high-quality reference images of the same identity to enhance identity fidelity in the restored outputs. However, most existing methods implicitly assume that the reference and degraded input are age-aligned, limiting their effectiveness in real-world scenarios where only cross-age references are available, such as historical photo restoration. This paper proposes MeInTime, a diffusion-based face restoration method that extends reference-based restoration from same-age to cross-age settings. Given one or few reference images along with an age prompt corresponding to the degraded input, MeInTime achieves faithful restoration with both identity fidelity and age consistency. Specifically, we decouple the modeling of identity and age conditions. During training, we focus solely on effectively injecting identity features through a newly introduced attention mechanism and introduce Gated Residual Fusion modules to facilitate the integration between degraded features and identity representations. At inference, we propose Age-Aware Gradient Guidance, a training-free sampling strategy, using an age-driven direction to iteratively nudge the identity-aware denoising latent toward the desired age semantic manifold. Extensive experiments demonstrate that MeInTime outperforms existing face restoration methods in both identity preservation and age consistency. Our code is available at: https://github.com/teer4/MeInTime
Related papers
- TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation [24.354251784441217]
TimeWeaver is a reference-based face restoration framework supporting cross-age references.<n>We decouple identity and age conditioning across training and inference.<n>Extensive experiments show that TimeWeaver surpasses existing methods in visual quality, identity preservation, and age consistency.
arXiv Detail & Related papers (2026-03-24T01:48:27Z) - Face Time Traveller : Travel Through Ages Without Losing Identity [22.930701520656005]
Face Time Traveller (FaceTT) is a diffusion-based framework that achieves high-fidelity, identity-consistent age transformation.<n>A tuning-free Angular Inversion method is proposed that efficiently maps real faces into the diffusion latent space for fast and accurate reconstruction.<n>Experiments on benchmark datasets and in-the-wild testset demonstrate that FaceTT achieves superior identity retention, background preservation and aging realism.
arXiv Detail & Related papers (2026-02-26T10:01:37Z) - Beyond Inference Intervention: Identity-Decoupled Diffusion for Face Anonymization [55.29071072675132]
Face anonymization aims to conceal identity information while preserving non-identity attributes.<n>We propose textbfIDsuperscript2Face, a training-centric anonymization framework.<n>We show that IDtextsuperscript2Face outperforms existing methods in visual quality, identity suppression, and utility preservation.
arXiv Detail & Related papers (2025-10-28T09:28:12Z) - WithAnyone: Towards Controllable and ID Consistent Image Generation [83.55786496542062]
Identity-consistent generation has become an important focus in text-to-image research.<n>We develop a large-scale paired dataset tailored for multi-person scenarios.<n>We propose a novel training paradigm with a contrastive identity loss that leverages paired data to balance fidelity with diversity.
arXiv Detail & Related papers (2025-10-16T17:59:54Z) - Personalized Face Super-Resolution with Identity Decoupling and Fitting [50.473357681579664]
In extreme degradation scenarios, critical attributes and ID information are often severely lost in the input image.<n>Existing methods tend to generate hallucinated faces under such conditions, producing restored images lacking authentic ID constraints.<n>We propose a novel FSR method with Identity Decoupling and Fitting (IDFSR) to enhance ID restoration under large scaling factors.
arXiv Detail & Related papers (2025-08-13T02:33:11Z) - Robust ID-Specific Face Restoration via Alignment Learning [14.7430941613282]
We present Robust ID-Specific Face Restoration (RIDFR), a novel ID-specific face restoration framework based on diffusion models.<n>RIDFR incorporates Alignment Learning, which aligns the restoration results from multiple references with the same identity in order to suppress the interference of ID-irrelevant face semantics.<n>Experiments demonstrate that our framework outperforms the state-of-the-art methods, reconstructing high-quality ID-specific results with high identity fidelity and demonstrating strong robustness.
arXiv Detail & Related papers (2025-07-15T03:16:12Z) - Reference-Guided Identity Preserving Face Restoration [54.10295747851343]
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration.<n>This paper introduces a novel approach that maximizes reference face utility for improved face restoration and identity preservation.
arXiv Detail & Related papers (2025-05-28T02:46:34Z) - RestorerID: Towards Tuning-Free Face Restoration with ID Preservation [18.022455458259305]
We propose a tuning-free method named RestorerID that incorporates ID preservation during face restoration.
To achieve this, we propose a unified framework to combine the ID injection with the base blind face restoration model.
Experimental results on the Celeb-Ref dataset and real-world scenarios demonstrate that RestorerID effectively delivers high-quality face restoration with ID preservation.
arXiv Detail & Related papers (2024-11-21T13:50:25Z) - When StyleGAN Meets Stable Diffusion: a $\mathscr{W}_+$ Adapter for
Personalized Image Generation [60.305112612629465]
Text-to-image diffusion models have excelled in producing diverse, high-quality, and photo-realistic images.
We present a novel use of the extended StyleGAN embedding space $mathcalW_+$ to achieve enhanced identity preservation and disentanglement for diffusion models.
Our method adeptly generates personalized text-to-image outputs that are not only compatible with prompt descriptions but also amenable to common StyleGAN editing directions.
arXiv Detail & Related papers (2023-11-29T09:05:14Z) - Age Gap Reducer-GAN for Recognizing Age-Separated Faces [72.26969872180841]
We propose a novel algorithm for matching faces with temporal variations caused due to age progression.
The proposed generative adversarial network algorithm is a unified framework that combines facial age estimation and age-separated face verification.
arXiv Detail & Related papers (2020-11-11T16:43:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.