MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration

Teer Song; Yue Zhang; Yu Tian; Ziyang Wang; Xianlin Zhang; Guixuan Zhang; Xuan Liu; Xueming Li

cs.CVMar 19, 2026

MeInTime: Bridging Age Gap in Identity-Preserving Face Restoration

Authors: Teer Song, Yue Zhang, Yu Tian, Ziyang Wang, Xianlin Zhang, Guixuan Zhang, Xuan Liu, Xueming Li,

Organizations: Beijing University of Posts and Telecommunications · Department of Computer Science and Technology, Institute for AI, Tsinghua University · Minzu University of China · Xiaomi Corporation

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

Figures & tables

Figure 1: Overview of MeInTime. (a) During training, identity features from reference images are extracted by Insightface ( Deng et al., 2019 ) , projected into face embeddings, and injected into UNet via decoupled cross-attention with a unified prompt “photo of a person”. Gated Residual Fusion (GRF) modules are introduced into each decoder block to facilitate feature fusion. The degraded input is processed following DiffBIR, which incorporates a restoration model ( Liang et al., 2021 ) and the proposed IRControlNet as structural guidance. We adopt the standard diffusion loss ( $\mathcal{L}_{\text{Diff}}$ ) as the training objective. (b) During inference, given the target age, the framework generates an age prompt ( e.g. , “photo of a 24-year-old person”) and performs two forward passes–with and without the age condition–to compute an age-aware gradient that iteratively refines the latent along the denoising process, enabling identity-preserving, age-controllable restoration.

Figure 2: The structure of Gated Residual Fusion module.

Figure 3: Visual comparison of identity-preserving restoration under different age control strategies.

Figure 4: Visual comparison of different optimization steps.

		Same-Age						Cross-Age
Method	Ref	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	FID $\downarrow$	IDS $\uparrow$	PSNR $\uparrow$	SSIM $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	FID $\downarrow$	IDS $\uparrow$	AGE $\downarrow$
CodeFormer		25.61	0.720	0.207	75.87	47.46	0.639	26.29	0.749	0.142	75.77	55.85	0.519	11.13
DR2+SPAR		21.97	0.681	0.312	71.31	69.43	0.213	21.71	0.646	0.357	64.52	95.05	0.118	15.52
DifFace		25.31	0.720	0.247	70.00	55.01	0.509	24.94	0.691	0.274	65.31	83.73	0.410	11.59
DMDNet	✓	25.76	0.730	0.228	73.92	55.48	0.703	25.76	0.712	0.233	72.94	66.37	0.621	13.27
RestorerID	✓	25.00	0.699	0.272	72.75	59.74	0.686	24.94	0.679	0.242	75.71	69.18	0.618	15.37

Table 1: Quantitative comparison with state-of-the-art methods on same-age and cross-age face restoration. The best results are shown in yellow , and the second-best are blue .

Panel of Figure 5 — Figure 5: User study results.

Figure 6: Identity Preservation vs. Age Consistency.

Figure 7: Comparison with state-of-the-art methods on same-age data. Zoom in for best view.

Figure 8: Comparison with state-of-the-art methods on cross-age data. Zoom in for best view.

Appendix figures & tables12 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 12 — Figure 12: Statistics of cross-age identity datasets.

Figure 13: Cross-age testing data instance.

Figure 14: Visualization of identity-token attention maps. Each column shows one restoration run with a different-age reference of the same identity. The second row gives the restored results, and the bottom row shows the averaged 16×16 identity-token attention maps across all steps.

Method	Ref	MUSIQ $\uparrow$	FID $\downarrow$	IDS $\uparrow$	AGE $\downarrow$
CodeFormer		70.17	56.53	0.263	9.67
DR2+SPAR		65.09	123.50	0.229	10.23
DifFace		56.28	119.78	0.295	11.83
DMDNet	✓	69.79	105.54	0.295	9.67
RestorerID	✓	74.25	76.31	0.467	15.14
Ref-LDM	✓	68.04	59.93	0.352	13.33

Appendix

Table 5: Quantitative comparison on the real-world dataset. The best results are shown in yellow , and the second-best are blue .

Figure 15: Comparison with state-of-the-art methods on real-world data. Zoom in for best view.

Method	Type	PSNR $\uparrow$	LPIPS $\downarrow$	MUSIQ $\uparrow$	IDS $\uparrow$	AGE $\downarrow$	Time(s)
HRFAE	GAN	21.57	0.308	67.31	0.602	10.23	0.18
SAM	GAN	18.09	0.457	60.33	0.347	8.77	0.45
FADING	Diffusion	22.28	0.270	66.52	0.584	8.05	133.41
MeInTime	Diffusion	24.53	0.208	74.33	0.655	6.89	43.37

Appendix

Table 6: Quantitative assessment of post-editing effects on FaceMe-restored images.

Figure 16: Post-hoc age editing applied to restored images from RestorerID, Ref-LDM, and FaceMe. Existing age-editing models either under-edit, over-edit, or damage identity, demonstrating the limitation of two-stage pipelines for cross-age restoration.

Methods	DifFace	DR2	RestorerID	Ref-LDM	FaceMe	MeInTime (N=0)	MeInTime (N=5)
Time(s)	5.96	1.23	7.74	1.79	7.65	7.26	43.37

Appendix

Table 7: Inference time comparison.

Figure 17: Over-sharpening artifacts when conditioning on high-age prompts.

Figure 18: Comparison with state-of-the-art methods on same-age data. Zoom in for best view.

Figure 19: Comparison with state-of-the-art methods on cross-age data. Zoom in for best view.

Figure 20: Example set used in the user study. For each question, participants are shown the ground-truth image, a reference image, and multiple restored results (A–E). They select the option that best satisfies the evaluation criterion for that question. The target age indicated at the top ( e.g. , “30-year-old”) is used only for age-related questions.

Image Restoration

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Recent progress in face restoration has shifted from visual fidelity to identity fidelity, driving a transition from reference-free to reference-based paradigms that condition restoration on reference images of the same person. However, these methods assume the reference and degraded input are age-aligned. When only cross-age references are available, as in historical restoration or missing-person retrieval, they fail to maintain age fidelity. To address this limitation, we propose TimeWeaver, the first reference-based face restoration framework supporting cross-age references. Given arbitrary reference images and a target-age prompt, TimeWeaver produces restorations with both identity fidelity and age consistency. Specifically, we decouple identity and age conditioning across training and inference. During training, the model learns an age-robust identity representation by fusing a global identity embedding with age-suppressed facial tokens via a transformer-based ID-Fusion module. During inference, two training-free techniques, Age-Aware Gradient Guidance and Token-Targeted Attention Boost, steer sampling toward desired age semantics, enabling precise adherence to the target-age prompt. Extensive experiments show that TimeWeaver surpasses existing methods in visual quality, identity preservation, and age consistency.

Abstract

Image Restoration

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