High-precision facial landmark detection (FLD) relies on high-resolution deep feature representations. However, low-resolution face images or the compression (via pooling or strided convolution) of originally high-resolution images hinder the learning of such features, thereby reducing FLD accuracy. Moreover, insufficient training data and imprecise annotations further degrade performance. To address these challenges, we propose a weakly-supervised framework called Supervision-by-Hallucination-and-Transfer (SHT) for more robust and precise FLD. SHT contains two novel mutually enhanced modules: Dual Hallucination Learning Network (DHLN) and Facial Pose Transfer Network (FPTN). By incorporating FLD and face hallucination tasks, DHLN is able to learn high-resolution representations with low-resolution inputs for recovering both facial structures and local details and generating more effective landmark heatmaps. Then, by transforming faces from one pose to another, FPTN can further improve landmark heatmaps and faces hallucinated by DHLN for detecting more accurate landmarks. To the best of our knowledge, this is the first study to explore weakly-supervised FLD by integrating face hallucination and facial pose transfer tasks. Experimental results of both face hallucination and FLD demonstrate that our method surpasses state-of-the-art techniques.

翻译：高精度面部关键点检测依赖于高分辨率的深度特征表示。然而，低分辨率人脸图像或原始高分辨率图像的下采样（通过池化或步长卷积）阻碍了此类特征的学习，从而降低了检测精度。此外，训练数据不足与标注不精确进一步削弱了性能。为解决这些挑战，我们提出了一种名为“基于幻觉与迁移的监督”的弱监督框架，以实现更鲁棒且精确的面部关键点检测。该框架包含两个新颖的相互增强模块：双重幻觉学习网络与面部姿态迁移网络。通过融合面部关键点检测与人脸幻觉任务，双重幻觉学习网络能够从低分辨率输入中学习高分辨率表示，以恢复面部整体结构与局部细节，并生成更有效的关键点热力图。随后，通过将人脸从一种姿态转换至另一种姿态，面部姿态迁移网络可进一步优化由双重幻觉学习网络生成的关键点热力图与幻觉人脸，从而检测出更精确的关键点。据我们所知，这是首个通过整合人脸幻觉与面部姿态迁移任务来探索弱监督面部关键点检测的研究。在人脸幻觉与面部关键点检测两方面的实验结果均表明，本方法超越了现有先进技术。