The large domain discrepancy between faces captured in polarimetric (or
conventional) thermal and visible domain makes cross-domain face recognition
quite a challenging problem for both human-examiners and computer vision
algorithms. Previous approaches utilize a two-step procedure (visible feature
estimation and visible image reconstruction) to synthesize the visible image
given the corresponding polarimetric thermal image. However, these are regarded
as two disjoint steps and hence may hinder the performance of visible face
reconstruction. We argue that joint optimization would be a better way to
reconstruct more photo-realistic images for both computer vision algorithms and
human-examiners to examine. To this end, this paper proposes a Generative
Adversarial Network-based Visible Face Synthesis (GAN-VFS) method to synthesize
more photo-realistic visible face images from their corresponding polarimetric
images. To ensure that the encoded visible-features contain more semantically
meaningful information in reconstructing the visible face image, a guidance
sub-network is involved into the training procedure. To achieve photo realistic
property while preserving discriminative characteristics for the reconstructed
outputs, an identity loss combined with the perceptual loss are optimized in
the framework. Multiple experiments evaluated on different experimental
protocols demonstrate that the proposed method achieves state-of-the-art
performance.

本文提出了一种基于生成对抗网络的可见面孔合成 (GAN-VFS) 方法，通过联合优化来自极化热成像的面孔图像。通过在训练过程中引入指导子网络来确保编码的可见特征包含更多的语义信息，结合恒等损失和感知损失实现兼顾光学真实属性和重建输出的区分特征，实验表明该方法达到了最先进的性能。

基于生成对抗网络的极化热脸可见脸合成

Generative Adversarial Network-based Synthesis of Visible Faces from  Polarimetric Thermal Faces

Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In this paper, we propose Stacked Generative Adversarial Networks
(StackGAN) to generate 256x256 photo-realistic images conditioned on text
descriptions. We decompose the hard problem into more manageable sub-problems
through a sketch-refinement process. The Stage-I GAN sketches the primitive
shape and colors of the object based on the given text description, yielding
Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text
descriptions as inputs, and generates high-resolution images with
photo-realistic details. It is able to rectify defects in Stage-I results and
add compelling details with the refinement process. To improve the diversity of
the synthesized images and stabilize the training of the conditional-GAN, we
introduce a novel Conditioning Augmentation technique that encourages
smoothness in the latent conditioning manifold. Extensive experiments and
comparisons with state-of-the-arts on benchmark datasets demonstrate that the
proposed method achieves significant improvements on generating photo-realistic
images conditioned on text descriptions.

本文提出 StackGAN，借助生成对抗网络，通过文本生成真实的 256x256 图像，经过两个 GAN 进行图像细节加强和修复，并且引入新的条件增强技术以提高图像多样性和稳定性，并取得了重大进展。

使用堆叠生成对抗网络实现文本到逼真图像的合成

StackGAN: Text to Photo-realistic Image Synthesis with Stacked  Generative Adversarial Networks

Fast and robust three-dimensional reconstruction of facial geometric
structure from a single image is a challenging task with numerous applications.
Here, we introduce a learning-based approach for reconstructing a
three-dimensional face from a single image. Recent face recovery methods rely
on accurate localization of key characteristic points. In contrast, the
proposed approach is based on a Convolutional-Neural-Network (CNN) which
extracts the face geometry directly from its image. Although such deep
architectures outperform other models in complex computer vision problems,
training them properly requires a large dataset of annotated examples. In the
case of three-dimensional faces, currently, there are no large volume data
sets, while acquiring such big-data is a tedious task. As an alternative, we
propose to generate random, yet nearly photo-realistic, facial images for which
the geometric form is known. The suggested model successfully recovers facial
shapes from real images, even for faces with extreme expressions and under
various lighting conditions.

本论文介绍了一种基于卷积神经网络和生成随机几乎逼真的面部图像的方法，可以通过单张照片快速、稳健地重建面部的三维几何结构，即使在极端表情和各种光照条件下也可以成功恢复面部形状。