1.Disentangled Person Image Generation
由于人的前景、背景和姿势信息等不同的图像因素之间的复杂相互作用，生成新颖而现实的人的图像是一项具有挑战性的任务。在这项工作中，我们的目标是基于一个新颖的两阶段重建管道生成这样的图像，该管道学习上述图像因子的分离表示，同时生成新颖的人物图像。首先，提出了一种多分支重构网络，将这三个因素分解编码到嵌入特征中，再结合嵌入特征对输入图像进行重构。其次，为了分别为每个因子将高斯噪声映射到所学的嵌入特征空间，以对抗的方式学习三个对应的映射函数。利用该框架，我们可以对输入图像的前景、背景和姿态进行操作，并可以对新的嵌入特征进行采样，以生成这样的有针对性的操作，从而对生成过程提供更多的控制。在1501市场和深度灰化数据集上的实验表明，我们的模型不仅能生成具有新前景、新背景和新姿势的真实人物图像，而且还可以控制生成的因素并插入州与州之间的。另一组在Market-1501上的实验表明，我们的模型也有利于人员重新识别任务。
Generating novel, yet realistic, images of persons is a challenging task due to the complex interplay between the different image factors, such as the foreground, background and pose information. In this work, we aim at generating such images based on a novel, two-stage reconstruction pipeline that learns a disentangled representation of the aforementioned image factors and generates novel person images at the same time. First, a multi-branched reconstruction network is proposed to disentangle and encode the three factors into embedding features, which are then combined to re-compose the input image itself. Second, three corresponding mapping functions are learned in an adversarial manner in order to map Gaussian noise to the learned embedding feature space, for each factor, respectively. Using the proposed framework, we can manipulate the foreground, background and pose of the input image, and also sample new embedding features to generate such targeted manipulations, that provide more control over the generation process. Experiments on the Market-1501 and Deepfashion datasets show that our model does not only generate realistic person images with new foregrounds, backgrounds and poses, but also manipulates the generated factors and interpolates the in-between states. Another set of experiments on Market-1501 shows that our model can also be beneficial for the person re-identification task.