Abstract
Blind Face Restoration aims to recover high-fidelity, detail-rich facial images from unknown degraded inputs, presenting significant challenges in preserving both identity and detail. Pre-trained diffusion models have been increasingly used as image priors to generate fine details. Still, existing methods often use fixed diffusion sampling timesteps and a global guidance scale, assuming uniform degradation. This limitation and potentially imperfect degradation kernel estimation frequently lead to under- or over-diffusion, resulting in an imbalance between fidelity and quality. We propose DynFaceRestore, a novel blind face restoration approach that learns to map any blindly degraded input to Gaussian blurry images. By leveraging these blurry images and their respective Gaussian kernels, we dynamically select the starting timesteps for each blurry image and apply closed-form guidance during the diffusion sampling process to maintain fidelity. Additionally, we introduce a dynamic guidance scaling adjuster that modulates the guidance strength across local regions, enhancing detail generation in complex areas while preserving structural fidelity in contours. This strategy effectively balances the trade-off between fidelity and quality. DynFaceRestore achieves state-of-the-art performance in both quantitative and qualitative evaluations, demonstrating robustness and effectiveness in blind face restoration.
Abstract (translated)
盲脸恢复的目标是从未知退化的输入中恢复高质量、细节丰富的面部图像，这在保持身份和细节的同时面临着重大挑战。预训练的扩散模型已经被越来越多地用作生成精细细节的图像先验。然而，现有的方法通常使用固定的扩散采样时间步长和全局指导尺度，假设退化是均匀的。这种限制以及可能不完美的退化核估计往往导致过度或不足的扩散，从而在保真度和质量之间造成不平衡。 我们提出了一种名为DynFaceRestore的新盲脸恢复方法，该方法能够将任何未知退化的输入映射到高斯模糊图像上。通过利用这些模糊图像及其相应的高斯核，我们可以为每个模糊图像动态选择起始时间步，并在扩散采样过程中应用闭式形式的指导来维持保真度。此外，我们引入了一个动态指导强度调节器，它可以跨局部区域调整引导强度，在复杂区域增强细节生成的同时保持轮廓中的结构保真度。这种策略有效地平衡了保真度和质量之间的权衡。 DynFaceRestore在定量和定性评估中均取得了最先进的性能，展示了其在盲脸恢复任务中的鲁棒性和有效性。
URL
https://arxiv.org/abs/2507.13797
