Corpus ID: 67734959

Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping

@article{Bazrafkan2018FaceSW,
  title={Face Synthesis with Landmark Points from Generative Adversarial Networks and Inverse Latent Space Mapping},
  author={Shabab Bazrafkan and Hossein Javidnia and Peter M. Corcoran},
  journal={arXiv: Image and Video Processing},
  year={2018}
}
Facial landmarks refer to the localization of fundamental facial points on face images. There have been a tremendous amount of attempts to detect these points from facial images however, there has never been an attempt to synthesize a random face and generate its corresponding facial landmarks. This paper presents a framework for augmenting a dataset in a latent Z-space and applied to the regression problem of generating a corresponding set of landmarks from a 2D facial dataset. The BEGAN… Expand
Synthesizing Coupled 3 D Face Modalities by Trunk-Branch Generative Adversarial Networks
Generating realistic 3D faces is of high importance for computer graphics and computer vision applications. Generally, research on 3D face generation revolves around linear statistical models of theExpand
Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
TLDR
This paper presents the first methodology that generates high-quality texture, shape, and normals jointly jointly, which can be used for photo-realistic synthesis and proposes a novel GAN that can generate data from different modalities while exploiting their correlations. Expand
Real-time Memory Efficient Large-pose Face Alignment via Deep Evolutionary Network
TLDR
A computationally efficient deep evolutionary model integrated with 3D Diffusion Heap Maps (DHM) that is 6 times faster than the state-of-the-art on CPU and 14 times on GPU. Expand
Block Mobilenet: Align Large-Pose Faces with <1MB Model Size
  • Bin Sun, Jun Li, Y. Fu
  • Computer Science
  • 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020)
  • 2020
TLDR
A novel Depthwise Separable Block (DSB) which consists of a depthwise block and a pointwise block which has better overall performance than the state-of-the-art methods. Expand
Generative Augmented Dataset and Annotation Frameworks for Artificial Intelligence (GADAFAI)
TLDR
A roadmap is introduced for a data-acquisition framework that can build the large synthetic datasets required to train AI systems from small seed datasets and example results are shown from preliminary work on biometric (facial) datasets. Expand
Convolutional Neural Network Implementation for Eye-Gaze Estimation on Low-Quality Consumer Imaging Systems
TLDR
A new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation, achieving better eye gaze accuracy with significantly fewer computational requirements. Expand
Efficient CNN Implementation for Eye-Gaze Estimation on Low-Power/Low-Quality Consumer Imaging Systems
TLDR
A new hardware friendly, convolutional neural network model with minimal computational requirements is introduced and assessed for efficient appearance-based gaze estimation, achieving better eye gaze accuracy with significantly fewer computational requirements. Expand
Synthesizing Game Audio Using Deep Neural Networks
TLDR
This paper examines a method of generating in-game audio using Generative Adversarial Networks, and compares this to traditional methods of synthesizing and augmenting audio. Expand

References

SHOWING 1-10 OF 17 REFERENCES
GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
TLDR
A novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented and a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures is proposed. Expand
Face aging with conditional generative adversarial networks
TLDR
This work proposes the first GAN-based method for automatic face aging and introduces a novel approach for “Identity-Preserving” optimization of GAN's latent vectors. Expand
Facial Landmark Detection with Tweaked Convolutional Neural Networks
TLDR
A novel CNN architecture, specialized to regress the facial landmark coordinates of faces in specific poses and appearances is described, shown to outperform existing landmark detection methods in an extensive battery of tests on the AFW, ALFW, and 300W benchmarks. Expand
Robust FEC-CNN: A High Accuracy Facial Landmark Detection System
TLDR
This work proposes an effective facial landmark detection system, recorded as Robust FEC-CNN (RFC), which achieves impressive results on facial landmarks detection in the wild and significantly outperforms the baseline approach APS. Expand
Face detection, pose estimation, and landmark localization in the wild
TLDR
It is shown that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures, in real-world, cluttered images. Expand
300 Faces in-the-Wild Challenge: The First Facial Landmark Localization Challenge
TLDR
The main goal of this challenge is to compare the performance of different methods on a new-collected dataset using the same evaluation protocol and the same mark-up and hence to develop the first standardized benchmark for facial landmark localization. Expand
A Fully End-to-End Cascaded CNN for Facial Landmark Detection
TLDR
This work proposes a Fully End-to-End Cascaded Convolutional Neural Network (FEC-CNN) for more promising facial landmark detection, which significantly improves the accuracy of landmark prediction. Expand
Image-to-Image Translation with Conditional Adversarial Networks
TLDR
Conditional adversarial networks are investigated as a general-purpose solution to image-to-image translation problems and it is demonstrated that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Expand
Face alignment by Explicit Shape Regression
TLDR
This paper presents a very efficient, highly accurate, “Explicit Shape Regression” approach for face alignment that significantly outperforms the state-of-the-art in terms of both accuracy and efficiency. Expand
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
TLDR
This work introduces a class of CNNs called deep convolutional generative adversarial networks (DCGANs), that have certain architectural constraints, and demonstrates that they are a strong candidate for unsupervised learning. Expand
...
1
2
...