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Unsupervised Texture Preserving Flow for Pose Guided Synthesis.

Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on hard-coded spatial transformations or thin-plate spline transformer and often overlook the complex non-rigid pose deformation and occlusion problems, thus failing to effectively preserve appearance information. In this paper, we propose an unsupervised optical flow learning scheme that directly learns to transfer the appearance details from the pose guided dataset. Based on a trained flow estimator for multi-scale feature-domain alignment, we design Garment2PoseNet, which is a unified network for coarse-to-fine synthesis. Experiments on the DeepFashion dataset demonstrate that our approach can generate quite realistic appearances. Furthermore, it compares favorably with the state-of-the-art methods on quantitative measurements.