Abstract
Old-photo face restoration poses significant challenges due to compounded degradations such as breakage, fading, and severe blur. Existing pre-trained diffusion-guided methods either rely on explicit degradation priors or global statistical guidance, which struggle with localized artifacts or face color. We propose Self-Supervised Selective-Guided Diffusion (SSDiff), which leverages pseudo-reference faces generated by a pre-trained diffusion model under weak guidance. These pseudo-labels exhibit structurally aligned contours and natural colors, enabling region-specific restoration via staged supervision: structural guidance applied throughout the denoising process and color refinement in later steps, aligned with the coarse-to-fine nature of diffusion. By incorporating face parsing maps and scratch masks, our method selectively restores breakage regions while avoiding identity mismatch. We further construct VintageFace, a 300-image benchmark of real old face photos with varying degradation levels. SSDiff outperforms existing GAN-based and diffusion-based methods in perceptual quality, fidelity, and regional controllability. Code link: this https URL.
Abstract (translated)
旧照片面部恢复面临着由断裂、褪色和严重模糊等多重退化造成的重大挑战。现有预训练的扩散引导方法要么依赖明确的降级先验，要么依靠全局统计指导，这些方法在处理局部伪影或面部颜色方面存在困难。我们提出了一种自监督选择性引导扩散（SSDiff）方法，该方法利用弱引导下由预训练扩散模型生成的伪参考面孔。这些伪标签表现出结构对齐的轮廓和自然的颜色，使区域特异性恢复通过分阶段监督得以实现：在整个去噪过程中应用结构性指导，并在后续步骤中进行颜色细化，与从粗到细的扩散性质相吻合。通过结合面部解析图和划痕掩码，我们的方法选择性地修复断裂区域，同时避免身份不符的问题。 我们进一步构建了VintageFace，这是一个包含300张具有不同退化程度的真实旧面孔照片的数据集基准。SSDiff在感知质量、保真度和区域可控性方面优于现有的基于GAN的方法和扩散方法。代码链接：[此处提供URL]。
URL
https://arxiv.org/abs/2510.12114
