FaceController: Controllable Attribute Editing for Face in the Wild
arXiv:2102.11464
Abstract
Face attribute editing aims to generate faces with one or multiple desired face attributes manipulated while other details are preserved. Unlike prior works such as GAN inversion, which has an expensive reverse mapping process, we propose a simple feed-forward network to generate high-fidelity manipulated faces. By simply employing some existing and easy-obtainable prior information, our method can control, transfer, and edit diverse attributes of faces in the wild. The proposed method can consequently be applied to various applications such as face swapping, face relighting, and makeup transfer. In our method, we decouple identity, expression, pose, and illumination using 3D priors; separate texture and colors by using region-wise style codes. All the information is embedded into adversarial learning by our identity-style normalization module. Disentanglement losses are proposed to enhance the generator to extract information independently from each attribute. Comprehensive quantitative and qualitative evaluations have been conducted. In a single framework, our method achieves the best or competitive scores on a variety of face applications.
arXiv commentAccepted to AAAI 2021
References in corpus (8)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
- FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
- Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
- Learning Latent Subspaces in Variational Autoencoders
- Mask-aware Photorealistic Face Attribute Manipulation