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.

为了解决面部数据集在处理侧面或俯仰面时深度学习模型性能下降的问题，本研究引入了一种名为 EFHQ 的新型数据集，包含了高达 45 万张在极端角度拍摄的高质量面部图像，通过训练 EFHQ 可以帮助模型在不同角度上得到良好的泛化性能，并提供了具有挑战性的交叉视角人脸验证基准，激发了在野外环境中严重姿态条件下的人脸识别研究。

EFHQ：多用途 ExtremePose-Face-HQ 数据集

EFHQ: Multi-purpose ExtremePose-Face-HQ dataset

Inferring 3D object structures from a single image is an ill-posed task due
to depth ambiguity and occlusion. Typical resolutions in the literature include
leveraging 2D or 3D ground truth for supervised learning, as well as imposing
hand-crafted symmetry priors or using an implicit representation to hallucinate
novel viewpoints for unsupervised methods. In this work, we propose a general
adversarial learning framework for solving Unsupervised 2D to Explicit 3D Style
Transfer (UE3DST). Specifically, we merge two architectures: the unsupervised
explicit 3D reconstruction network of Wu et al.\ and the Generative Adversarial
Network (GAN) named StarGAN-v2. We experiment across three facial datasets
(Basel Face Model, 3DFAW and CelebA-HQ) and show that our solution is able to
outperform well established solutions such as DepthNet in 3D reconstruction and
Pix2NeRF in conditional style transfer, while we also justify the individual
contributions of our model components via ablation. In contrast to the
aforementioned baselines, our scheme produces features for explicit 3D
rendering, which can be manipulated and utilized in downstream tasks.

本文旨在提出一种解决由于深度歧义和遮挡而导致的从单个图像推断三维物体结构的无监督方法 ——Unsupervised 2D 到 Explicit 3D Style Transfer (UE3DST)，我们提出了一个通用的对抗学习框架，通过将 Wu 等人的无监督明确 3D 重建网络和 StarGAN-v2 之类的生成对抗网络 (GAN) 进行合并，我们在三个面部数据集 (Basel Face Model、3DFAW 和 CelebA-HQ) 上进行实验，结果表明我们的解决方案能够胜过 DepthNet 在 3D 重建和 Pix2NeRF 在条件样式转移方面已经建立的解决方案，相比之下，我们的方案产生了用于明确 3D 渲染的特征，可以在下游任务中进行操作和利用。