Self-portraits captured from a short distance might look unnatural or even unattractive due to heavy distortions making facial features malformed, and ill-placed head poses. In this paper, we propose SUPER, a novel method of eliminating distortions and adjusting head pose in a close-up face crop. We perform 3D GAN inversion for a facial image by optimizing camera parameters and face latent code, which gives a generated image. Besides, we estimate depth from the obtained latent code, create a depth-induced 3D mesh, and render it with updated camera parameters to obtain a warped portrait. Finally, we apply the visibility-based blending so that visible regions are reprojected, and occluded parts are restored with a generative model. Experiments on face undistortion benchmarks and on our self-collected Head Rotation dataset (HeRo), show that SUPER outperforms previous approaches both qualitatively and quantitatively, opening new possibilities for photorealistic selfie editing.

翻译：近距离拍摄的自拍肖像常因严重畸变导致面部特征变形及头部姿态不当，从而显得不自然甚至缺乏美感。本文提出SUPER方法，一种在近距离人脸裁剪图中消除畸变并调整头部姿态的新技术。我们通过优化相机参数与面部潜码对输入人脸图像执行3D GAN反演，获得生成图像。此外，从所得潜码中估计深度信息，构建深度诱导的3D网格，并通过更新相机参数进行渲染得到形变后的肖像。最后采用基于可见性的融合策略：可见区域通过重投影处理，而遮挡部分则通过生成模型进行修复。在人脸畸变校正基准测试及我们自建的头部旋转数据集（HeRo）上的实验表明，SUPER在定性与定量评估中均优于现有方法，为真实感自拍编辑开辟了新途径。