用对齐学习提升人脸修复的身份一致性，解决模糊输入下的身份丢失问题。
Robust ID-Specific Face Restoration via Alignment Learning
- 双模块并行输入内容与身份信息，通过扩散模型重建人脸。
- 多参考对齐学习有效抑制姿态、表情等无关语义干扰。
- 在低质量输入下仍保持高身份保真度，适合真实场景修复。
最近的人脸修复进展借助多种扩散先验显著提升了视觉质量，但由身份模糊输入和随机生成过程带来的身份不确定性仍未解决。为此，我们提出鲁棒的特定身份人脸修复框架RIDFR，基于扩散模型结合两个并行条件模块：内容注入模块输入严重退化的图像，身份注入模块引入给定图像中的特定身份信息。随后，RIDFR采用对齐学习机制，将多个同身份参考图像的修复结果进行对齐，以抑制无关面部语义（如姿态、表情、妆容、发型）的干扰。实验表明，该框架优于当前最先进方法，在高身份保真度下实现高质量修复，并展现出强鲁棒性。
原文摘要 · Abstract (English)
The latest developments in Face Restoration have yielded significant advancements in visual quality through the utilization of diverse diffusion priors. Nevertheless, the uncertainty of face identity introduced by identity-obscure inputs and stochastic generative processes remains unresolved. To address this challenge, we present Robust ID-Specific Face Restoration (RIDFR), a novel ID-specific face restoration framework based on diffusion models. Specifically, RIDFR leverages a pre-trained diffusion model in conjunction with two parallel conditioning modules. The Content Injection Module inputs the severely degraded image, while the Identity Injection Module integrates the specific identity from a given image. Subsequently, 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 (e.g. pose, expression, make-up, hair style). 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.
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