DGPose Deep Generative Models for Human Body Analysis

Deep generative modelling for human body analysis is an emerging problem with many interesting applications . But the latent space learned by such approaches is typically not interpretable, resulting in less flexibility… In this work, we present deep generative models 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 . 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 . We are confident that our models are capable of mapping images to interpretable latent representations but also able to map these representations back to the image space. Therefore, the Semi-DGPose aims for the joint understanding and generation of people in images. It is not only capable of

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Keywords : pose - body - models - deep - generative -

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