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摘要翻译：
面部地标是指面部基本点在人脸图像上的定位。已经有大量的尝试从人脸图像中检测这些点，但是从来没有尝试合成一个随机的人脸并生成相应的人脸地标。本文提出了一种在潜在Z空间中扩充数据集的框架，并应用于从二维人脸数据集生成相应地标集的回归问题。BARID框架已经被用来训练一个来自CelebA数据库的人脸生成器。利用Adam优化器实现生成器的逆，生成对应于每个人脸图像的潜在向量，并训练轻量级深度神经网络将潜在Z空间向量映射到地标空间。初步结果是有希望的，并提供了一个通用的方法，以增加注释图像数据集与额外的中间样本。
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英文标题：
《Face Synthesis with Landmark Points from Generative Adversarial Networks
and Inverse Latent Space Mapping》
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作者：
Shabab Bazrafkan, Hossein Javidnia, Peter Corcoran
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最新提交年份：
2018
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分类信息：
一级分类：Electrical Engineering and Systems Science 电气工程与系统科学
二级分类：Image and Video Processing 图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
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 framework has been used to train a face generator from CelebA database. The inverse of the generator is implemented using an Adam optimizer to generate the latent vector corresponding to each facial image, and a lightweight deep neural network is trained to map latent Z-space vectors to the landmark space. Initial results are promising and provide a generic methodology to augment annotated image datasets with additional intermediate samples.
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PDF链接：
https://arxiv.org/pdf/1802.0039
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