Close-up facial images captured at short distances often suffer from perspective distortion, resulting in exaggerated facial features and unnatural/unattractive appearances. We propose a simple yet effective method for correcting perspective distortions in a single close-up face. We first perform GAN inversion using a perspective-distorted input facial image by jointly optimizing the camera intrinsic/extrinsic parameters and face latent code. To address the ambiguity of joint optimization, we develop starting from a short distance, optimization scheduling, reparametrizations, and geometric regularization. Re-rendering the portrait at a proper focal length and camera distance effectively corrects perspective distortions and produces more natural-looking results. Our experiments show that our method compares favorably against previous approaches qualitatively and quantitatively. We showcase numerous examples validating the applicability of our method on in-the-wild portrait photos. We will release our code and the evaluation protocol to facilitate future work.
@article{arxiv.2302.12253,
title = {DisCO: Portrait Distortion Correction with Perspective-Aware 3D GANs},
author = {Zhixiang Wang and Yu-Lun Liu and Jia-Bin Huang and Shin'ichi Satoh and Sizhuo Ma and Gurunandan Krishnan and Jian Wang},
journal= {arXiv preprint arXiv:2302.12253},
year = {2023}
}