Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility. In this work, we present deep generative models for human body analysis in which the body pose and the visual appearance are disentangled. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without specific training for such a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose, have different characteristics. In the first, body pose labels are taken as conditioners, from a fully-supervised training set. In the second, our structured semi-supervised approach allows for pose estimation to be performed by the model itself and relaxes the need for labelled data. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. We compare our models with relevant baselines, the ClothNet-Body and the Pose Guided Person Generation networks, demonstrating their merits on the Human3.6M, ChictopiaPlus and DeepFashion benchmarks.

中文翻译：

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DGPose：用于人体分析的深度生成模型

用于人体分析的深度生成建模是一个具有许多有趣应用的新兴问题。然而，通过这种方法学习的潜在空间通常是不可解释的，导致灵活性较低。在这项工作中，我们提出了用于人体分析的深度生成模型，其中身体姿势和视觉外观被解开。这种解开允许独立操纵姿势和外观，因此无需针对此类任务进行专门培训即可实现姿势转移等应用。我们提出的模型，Conditional-DGPose 和 Semi-DGPose，具有不同的特征。首先，身体姿势标签被作为调节器，来自一个完全监督的训练集。在第二，我们的结构化半监督方法允许由模型本身执行姿态估计，并放宽对标记数据的需求。因此，Semi-DGPose旨在实现图像中人的共同理解和生成。它不仅能够将图像映射到可解释的潜在表示，还能够将这些表示映射回图像空间。我们将我们的模型与相关基线、ClothNet-Body 和 Pose Guided Person Generation 网络进行比较，证明它们在 Human3.6M、ChictopiaPlus 和 DeepFashion 基准测试中的优点。它不仅能够将图像映射到可解释的潜在表示，还能够将这些表示映射回图像空间。我们将我们的模型与相关基线、ClothNet-Body 和 Pose Guided Person Generation 网络进行比较，证明它们在 Human3.6M、ChictopiaPlus 和 DeepFashion 基准测试中的优点。它不仅能够将图像映射到可解释的潜在表示，还能够将这些表示映射回图像空间。我们将我们的模型与相关基线、ClothNet-Body 和 Pose Guided Person Generation 网络进行比较，证明它们在 Human3.6M、ChictopiaPlus 和 DeepFashion 基准测试中的优点。