View synthesis aims at generating a novel, unseen view of an object. This is a challenging task in the presence of occlusions and asymmetries. In this paper, we present View-Disentangled Generator (VDG), a two-stage deep network for pose-guided human-image generation that performs coarse view prediction followed by a refinement stage. In the first stage, the network predicts the output from a target human pose, the source-image and the corresponding human pose, which are processed in different branches separately. This enables the network to learn a disentangled representation from the source and target view. In the second stage, the coarse output from the first stage is refined by adversarial training. Specifically, we introduce a masked version of the structural similarity loss that facilitates the network to focus on generating a higher quality view. Experiments on Market-1501 and DeepFashion demonstrate the effectiveness of the proposed generator.
Pose-Guided Human Image Synthesis by View Disentanglement and Enhanced Weighting Loss
Lakhal, Mohamed;Lanz, Oswald
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2018-01-01
Abstract
View synthesis aims at generating a novel, unseen view of an object. This is a challenging task in the presence of occlusions and asymmetries. In this paper, we present View-Disentangled Generator (VDG), a two-stage deep network for pose-guided human-image generation that performs coarse view prediction followed by a refinement stage. In the first stage, the network predicts the output from a target human pose, the source-image and the corresponding human pose, which are processed in different branches separately. This enables the network to learn a disentangled representation from the source and target view. In the second stage, the coarse output from the first stage is refined by adversarial training. Specifically, we introduce a masked version of the structural similarity loss that facilitates the network to focus on generating a higher quality view. Experiments on Market-1501 and DeepFashion demonstrate the effectiveness of the proposed generator.File | Dimensione | Formato | |
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10.1007@978-3-030-11012-330.pdf
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