Method overview
Portrait photos captured from a near-range distance often suffer from undesired perspective distortions. DisCO corrects these perspective distortions and synthesizes more pleasant views by virtually enlarging focal length and camera-to-subject distance.
Close-up facial images captured at close 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 focal length reparametrization, optimization scheduling, and geometric regularization. Re-rendering the portrait at a proper focal length and camera distance effectively corrects these distortions and produces more natural-looking results. Our experiments show that our method compares favorably against previous approaches regarding visual quality. We showcase numerous examples validating the applicability of our method on portrait photos in the wild.
Visual comparisons on our collected images
We conduct comparisons on CMDP. Results of Fried+ is borrowed from their demo page. #Fried+ denotes our re-implementation of Fried+.
Special thanks to Yajie Zhao for providing their results and data; Ohad Fried for sharing their results on web.
Our collected in-the-wild images are from internet under common creative. Sources are here.
