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This paper proposes the idea of using a generative adversarial network (GAN) to assist a novice user in designing real-world shapes with a simple interface.
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Large scale comprehensive 3d shape retrieval
B. Li, Y. Lu, C. Li, A. Godil, T. Schreck, M. Aono, Q. Chen, N. K. Chowdhury, B. Fang, T. Furuya, H. Johan, R. Kosaka, H. Koyanagi, R. Ohbuchi, and A. Tatsuma · 2014
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Interactive design of probability density functions for shape grammars
M. Dang, S. Lienhard, D. Ceylan, B. Neubert, P. Wonka, and M. Pauly · 2015
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C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan, and L. J. Guibas · 2016
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Unsupervised learning of 3d structure from images
D. J. Rezende, S. M. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess · 2016
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Vconv-dae: Deep volumetric shape learning without object labels
A. Sharma, O. Grau, and M. Fritz · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, W. T. Freeman, and J. B. Tenenbaum · 2016
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