The existing facial datasets, while having plentiful images at near frontal views, lack images with extreme head poses, leading to the downgraded performance of deep learning models when dealing with profile or pitched faces. This work aims to address this gap by introducing a novel dataset named Extreme Pose Face High-Quality Dataset (EFHQ), which includes a maximum of 450k high-quality images of faces at extreme poses. To produce such a massive dataset, we utilize a novel and meticulous dataset processing pipeline to curate two publicly available datasets, VFHQ and CelebV-HQ, which contain many high-resolution face videos captured in various settings. Our dataset can complement existing datasets on various facial-related tasks, such as facial synthesis with 2D/3D-aware GAN, diffusion-based text-to-image face generation, and face reenactment. Specifically, training with EFHQ helps models generalize well across diverse poses, significantly improving performance in scenarios involving extreme views, confirmed by extensive experiments. Additionally, we utilize EFHQ to define a challenging cross-view face verification benchmark, in which the performance of SOTA face recognition models drops 5-37\% compared to frontal-to-frontal scenarios, aiming to stimulate studies on face recognition under severe pose conditions in the wild.

翻译：现有面部数据集虽包含大量近正面视角图像，但缺乏极端头部姿态样本，导致深度学习模型在处理侧脸或俯仰面部时性能下降。本研究通过构建名为极端姿态人脸高清数据集（EFHQ）的新型数据集填补这一空白，该数据集包含最多45万张极端姿态下的高质量人脸图像。为生成如此大规模数据集，我们采用新颖且精细化的数据流水线，对两个公开可用数据集VFHQ和CelebV-HQ进行筛选处理，这两个数据集包含大量在不同场景中采集的高分辨率人脸视频。我们的数据集可增强现有数据集在多项面部相关任务中的表现，包括基于2D/3D感知GAN的人脸合成、基于扩散模型的文本到图像人脸生成，以及人脸再现。具体而言，实验证实EFHQ训练有助于模型跨不同姿态实现良好泛化，在极端视角场景中显著提升性能。此外，我们利用EFHQ构建具有挑战性的跨视角人脸验证基准，在该基准中，当前最佳人脸识别模型性能相较正面-正面场景下降5-37%，旨在推动野外严重姿态条件下的人脸识别研究。