FaceShapeGene: A Disentangled Shape Representation for Flexible Face Image Editing
arXiv:1905.01920
Abstract
Existing methods for face image manipulation generally focus on editing the expression, changing some predefined attributes, or applying different filters. However, users lack the flexibility of controlling the shapes of different semantic facial parts in the generated face. In this paper, we propose an approach to compute a disentangled shape representation for a face image, namely the FaceShapeGene. The proposed FaceShapeGene encodes the shape information of each semantic facial part separately into a 1D latent vector. On the basis of the FaceShapeGene, a novel part-wise face image editing system is developed, which contains a shape-remix network and a conditional label-to-face transformer. The shape-remix network can freely recombine the part-wise latent vectors from different individuals, producing a remixed face shape in the form of a label map, which contains the facial characteristics of multiple subjects. The conditional label-to-face transformer, which is trained in an unsupervised cyclic manner, performs part-wise face editing while preserving the original identity of the subject. Experimental results on several tasks demonstrate that the proposed FaceShapeGene representation correctly disentangles the shape features of different semantic parts. %In addition, we test our system on several novel part-wise face editing tasks. Comparisons to existing methods demonstrate the superiority of the proposed method on accomplishing novel face editing tasks.
References in corpus (22)
- Adam: A Method for Stochastic Optimization
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- Deep Residual Learning for Image Recognition
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Stochastic Backpropagation and Approximate Inference in Deep Generative Models
- Progressive Growing of GANs for Improved Quality, Stability, and Variation
- Improved Training of Wasserstein GANs
- Going Deeper with Convolutions
- NICE: Non-linear Independent Components Estimation
- Improved Techniques for Training GANs
- Density estimation using Real NVP
- BEGAN: Boundary Equilibrium Generative Adversarial Networks
- DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs
- Autoencoding beyond pixels using a learned similarity metric
- High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
- Multimodal Unsupervised Image-to-Image Translation
- DRIT++: Diverse Image-to-Image Translation via Disentangled Representations
- Least Squares Generative Adversarial Networks
- Beyond Face Rotation: Global and Local Perception GAN for Photorealistic and Identity Preserving Frontal View Synthesis
- Deep Learning Face Attributes in the Wild
- Modular Generative Adversarial Networks