Ryota Natsume, Tatsuya Yatagawa, Shigeo Morishima

FSNet: An Identity-aware Generative Model for Image-based Face Swapping

ACCV (6) 2018: 117-132, DOI:10.1007/978-3-030-20876-9_8

Asian Conference of Computer Vision 2018


This paper presents FSNet, a deep generative model for image-based face swapping. Traditionally, face-swapping methods are based on three-dimensional morphable models (3DMMs), and facial textures are replaced between the estimated three-dimensional (3D) geometries in two images of different individuals 픽시브 일괄. However, the estimation of 3D geometries along with different lighting conditions using 3DMMs is still a difficult task. We herein represent the face region with a latent variable that is assigned with the proposed deep neural network (DNN) instead of facial textures 해리포터와 마법사의 돌 게임. The proposed DNN synthesizes a face-swapped image using the latent variable of the face region and another image of the non-face region. The proposed method is not required to fit to the 3DMM; additionally, it performs face swapping only by feeding two face images to the proposed network 굿윌헌팅 한글자막 다운로드. Consequently, our DNN-based face swapping performs better than previous approaches for challenging inputs with different face orientations and lighting conditions google map api 다운로드. Through several experiments, we demonstrated that the proposed method performs face swapping in a more stable manner than the state-of-the-art method, and that its results are compatible with the method thereof Download Pokémon Pikachu.