Abstract
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 (translated)
近期人脸修复领域的进展已从视觉保真度转向身份保真度，推动了从无参考图像到参考图像驱动范式的转变，即依据同一人的参考图像进行修复。然而，这些方法假设参考图像与退化输入在年龄上对齐。当仅存在跨年龄参考图像时（如历史影像修复或寻人检索场景），现有方法无法保持年龄保真度。为应对此局限，我们提出TimeWeaver——首个支持跨年龄参考图像的参考型人脸修复框架。给定任意参考图像与目标年龄提示词，TimeWeaver可生成兼具身份保真度与年龄一致性的修复结果。具体而言，我们在训练与推理阶段解耦身份与年龄条件：训练时，模型通过基于Transformer的ID融合模块，将全局身份嵌入与年龄抑制的面部令牌相融合，学习抗年龄干扰的身份表征；推理时，采用两种无需训练的技术——年龄感知梯度引导与令牌定向注意力增强，引导采样过程朝向目标年龄语义，实现精确遵循目标年龄提示词。大量实验表明，TimeWeaver在视觉质量、身份保持与年龄一致性上均超越现有方法。
URL
https://arxiv.org/abs/2603.22701
