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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.22701 |
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| _version_ | 1866915886027243520 |
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| author | Song, Teer Zhang, Yue Tian, Yu Wang, Ziyang Zhang, Xianlin Zhang, Guixuan Liu, Xuan Li, Xueming Zhang, Yasen |
| author_facet | Song, Teer Zhang, Yue Tian, Yu Wang, Ziyang Zhang, Xianlin Zhang, Guixuan Liu, Xuan Li, Xueming Zhang, Yasen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22701 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation Song, Teer Zhang, Yue Tian, Yu Wang, Ziyang Zhang, Xianlin Zhang, Guixuan Liu, Xuan Li, Xueming Zhang, Yasen Computer Vision and Pattern Recognition 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. |
| title | TimeWeaver: Age-Consistent Reference-Based Face Restoration with Identity Preservation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.22701 |