Mask-Guided Portrait Editing with Conditional GANs

Abstract

Portrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To address these issues, we propose a novel end-to-end learning framework that leverages conditional GANs guided by provided face masks for generating faces. The framework learns feature embeddings for every face component (e.g., mouth, hair, eye), separately, contributing to better correspondences for image translation, and local face editing. With the mask, our network is available to many applications, like face synthesis driven by mask, face Swap+ (including hair in swapping), and local manipulation. It can also boost the performance of face parsing a bit as an option of data augmentation.

To appear in CVPR2019

References in corpus (10)

• Conditional Generative Adversarial Nets
• 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
• Multimodal Unsupervised Image-to-Image Translation
• Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
• Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
• Convolutional Network for Attribute-driven and Identity-preserving Human Face Generation
• Visual Attribute Transfer through Deep Image Analogy
• FaceShop: Deep Sketch-based Face Image Editing
• Learning Face Age Progression: A Pyramid Architecture of GANs

Cited by in corpus (5)

• SegAttnGAN: Text to Image Generation with Segmentation Attention
• Dual Attention GANs for Semantic Image Synthesis
• Disentangled Face Attribute Editing via Instance-Aware Latent Space Search
• ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
• Collaging Class-specific GANs for Semantic Image Synthesis