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XingGAN for Person Image Generation
We propose a novel Generative Adversarial Network (XingGAN or CrossingGA...
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Progressive Pose Attention Transfer for Person Image Generation
This paper proposes a new generative adversarial network for pose transf...
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Deep Image Spatial Transformation for Person Image Generation
Pose-guided person image generation is to transform a source person imag...
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Person image generation with semantic attention network for person re-identification
Pose variation is one of the key factors which prevents the network from...
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Deep Spatial Transformation for Pose-Guided Person Image Generation and Animation
Pose-guided person image generation and animation aim to transform a sou...
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Adversarial Generation of Continuous Images
In most existing learning systems, images are typically viewed as 2D pix...
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SpatialSense: An Adversarially Crowdsourced Benchmark for Spatial Relation Recognition
Understanding the spatial relations between objects in images is a surpr...
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Bipartite Graph Reasoning GANs for Person Image Generation
We present a novel Bipartite Graph Reasoning GAN (BiGraphGAN) for the challenging person image generation task. The proposed graph generator mainly consists of two novel blocks that aim to model the pose-to-pose and pose-to-image relations, respectively. Specifically, the proposed Bipartite Graph Reasoning (BGR) block aims to reason the crossing long-range relations between the source pose and the target pose in a bipartite graph, which mitigates some challenges caused by pose deformation. Moreover, we propose a new Interaction-and-Aggregation (IA) block to effectively update and enhance the feature representation capability of both person's shape and appearance in an interactive way. Experiments on two challenging and public datasets, i.e., Market-1501 and DeepFashion, show the effectiveness of the proposed BiGraphGAN in terms of objective quantitative scores and subjective visual realness. The source code and trained models are available at https://github.com/Ha0Tang/BiGraphGAN.
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