Abstract
Face super-resolution aims to recover high-quality facial images from severely degraded low-resolution inputs, but remains challenging due to the loss of fine structural details and identity-specific features. This work introduces SwinIFS, a landmark-guided super-resolution framework that integrates structural priors with hierarchical attention mechanisms to achieve identity-preserving reconstruction at both moderate and extreme upscaling factors. The method incorporates dense Gaussian heatmaps of key facial landmarks into the input representation, enabling the network to focus on semantically important facial regions from the earliest stages of processing. A compact Swin Transformer backbone is employed to capture long-range contextual information while preserving local geometry, allowing the model to restore subtle facial textures and maintain global structural consistency. Extensive experiments on the CelebA benchmark demonstrate that SwinIFS achieves superior perceptual quality, sharper reconstructions, and improved identity retention; it consistently produces more photorealistic results and exhibits strong performance even under 8x magnification, where most methods fail to recover meaningful structure. SwinIFS also provides an advantageous balance between reconstruction accuracy and computational efficiency, making it suitable for real-world applications in facial enhancement, surveillance, and digital restoration. Our code, model weights, and results are available at this https URL.
Abstract (translated)
面部超分辨率技术旨在从严重降级的低分辨率输入中恢复高质量的人脸图像，但由于精细结构细节和特定身份特征的丢失，这一任务仍然具有挑战性。这项工作引入了SwinIFS，这是一种基于标志点引导的超级分辨率框架，它结合了结构先验知识与分层注意力机制，在适度和极端放大的情况下都能实现保真度重建。该方法将关键面部标志点的密集高斯热图融入输入表示中，使得网络能够从处理的早期阶段就开始关注语义重要的面部区域。采用了一个紧凑型Swin Transformer骨干网来捕捉长距离上下文信息同时保持局部几何形状，使模型能够恢复细微的人脸纹理并维持全局结构的一致性。 在CelebA基准测试上的广泛实验表明，SwinIFS实现了卓越的感知质量、更清晰的重建效果以及改进的身份保留能力。该方法持续生成更具真实感的结果，并且即使在8倍放大时也能展现出强大的性能，而大多数其他方法在这种极端情况下无法恢复有意义的结构。 此外，SwinIFS在重建精度和计算效率之间提供了一个有利的平衡点，使其适合用于面部美化、监控以及数字修复等现实世界的应用场景中。我们的代码、模型权重及结果可在以下链接获取：[https URL]（请将[https URL]替换为实际提供的URL）。
URL
https://arxiv.org/abs/2601.01406
