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Deep generative modelling for human body analysis is an emerging problem withmany interesting applications. However, the latent space learned by suchapproaches is typically not interpretable, resulting in less flexibility. Inthis work, we present deep generative models for human body analysis in whichthe body pose and the visual appearance are disentangled. Such adisentanglement allows independent manipulation of pose and appearance, andhence enables applications such as pose-transfer without specific training forsuch a task. Our proposed models, the Conditional-DGPose and the Semi-DGPose,have different characteristics. In the first, body pose labels are taken asconditioners, from a fully-supervised training set. In the second, ourstructured semi-supervised approach allows for pose estimation to be performedby the model itself and relaxes the need for labelled data. Therefore, theSemi-DGPose aims for the joint understanding and generation of people inimages. It is not only capable of mapping images to interpretable latentrepresentations but also able to map these representations back to the imagespace. We compare our models with relevant baselines, the ClothNet-Body and thePose Guided Person Generation networks, demonstrating their merits on theHuman3.6M, ChictopiaPlus and DeepFashion benchmarks.

DGPose: Deep Generative Models for Human Body Analysis