EFHQ: Multi-Purpose ExtremePose-Face-HQ Dataset

A profile portrait image of a person. Figure 1. Benefits of our proposed dataset (EFHQ). Standard large-scale facial datasets have most images at near frontal views, causing inferior performance of trained models on downstream tasks when dealing with extreme head poses. For instance, the trained 2D image generators and text-to-image ones often produce only near frontal faces, while the 3D face generators and face reenactment methods often show distorted outputs at profile views. The recently proposed dataset LPFF [47] partially handles that issue by providing complementary images at extreme head poses for only 2D and 3D image generation tasks. Our proposed dataset EFHQ provides high-quality extreme-pose images to complement a wide range of face-related tasks. It supports 2D and 3D image generation, with generally better diversity than LPFF. EFHQ also helps correct the outputs of text-to-image generation and face reenactment at extreme views. Finally, EFHQ provides a more challenging pose-based face verification benchmark to better assess the quality of face recognition networks.