Combining Generative and Geometry Priors for Wide-Angle Portrait Correction

Wide-angle lens distortion in portrait photography presents a significant challenge for capturing photo-realistic and aesthetically pleasing images. Such distortions are especially noticeable in face regions. To rectify facial distortions for a more natural appearance, we propose encapsulating the generative face prior derived from pre-trained StyleGAN. This prior is then leveraged to facilitate the correction of facial regions. For the non-face background, a notable central symmetry relationship exists in the wide-angle imaging process, yet it has not been explored in the correction process. This geometry prior motivates us to introduce a novel constraint with the explicit aim of preserving symmetry throughout the correction process, thereby contributing to a more visually appealing and natural correction in the non-face region. Experiments demonstrate that our approach outperforms previous methods by a large margin, excelling not only in quantitative measures such as line straightness and shape consistency metrics but also in terms of perceptual visual quality. Our source code and model will be made publicly available.