Attribute-Conditioned Face Swapping Network for Low-Resolution Images

Ang Li, Jian Hu, Chilin Fu, Xiaolu Zhang, Jun Zhou

Abstract: Deep learning based face swapping technologies have opened new frontiers for entertainment industries while pose novel threats to identity security. Applying face swapping to real-world products, as well as defending against its misuse, rely on the capacity to generate high quality face swapped images from realistic scenarios where high resolution images are hard to come by. To this end, we need to address the drawbacks of existing methods, especially on their lacking on maintaining the detail attributes and their dependency on high resolution images as inputs. In this paper, we propose a novel Attribute-Conditioned Face Swapping Network (AFSNet) to preserve attributes and handle low resolution images. Specifically, we use an Image Enhancement Network (IEN) to restore high resolution images from low resolution images and a Face Exchange Module (FEM) to swap the faces. In the FEM, we improve the fidelities of the generated images by using a novel multi-domain feature fusion module (MDFFM) to integrate the identity feature, context feature, IEN feature, and attribute vector to obtain the final image. We also design an attribute transfer loss to promote the consistency of the attributes such as beards and youth between the source and swapped images. The experiments demonstrate our method’s superior performance compared with the state-of-the-art methods.