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This paper proposes the novel Pose Guided Person Generation Network (PG$^2$) that allows to synthesize person images in arbitrary poses, based on an image of that person and a novel pose.
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Joao Carreira, Pulkit Agrawal, Katerina Fragkiadaki, and Jitendra Malik · 2016
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Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Xu Jia, Amir Ghodrati, Marco Pedersoli, and Tinne Tuytelaars · 2016
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Improved techniques for training gans
Tim Salimans, Ian J. Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Neural autoregressive distribution estimation
Benigno Uria, Marc-Alexandre Côté, Karol Gregor, Iain Murray, and Hugo Larochelle · 2016
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Conditional image generation with pixelcnn decoders
Aäron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Koray Kavukcuoglu, Oriol Vinyals, and Alex Graves · 2016
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Pixel recurrent neural networks
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Attribute2image: Conditional image generation from visual attributes
Xinchen Yan, Jimei Yang, Kihyuk Sohn, and Honglak Lee · 2016
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Wasserstein GAN
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Multi-view image generation from a single-view
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