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A novel conditional auto-encoder (AE) is then augmented to act as a part-level refiner. The GAN, associated with additional local discriminators and quality losses, synthesizes a voxel-based model, and assigns the voxels with part labels that are represented in separate channels. The AE is trained to amend the initial synthesis of the parts, yielding more plausible part geometries. We also introduce new means to measure and evaluate the performance of an adversarial generative model. We demonstrate that our global-to-local generative model produces significantly better results than a plain three-dimensional GAN, in terms of both their shape variety and the distribution with respect to the training data.<\/jats:p>","DOI":"10.1145\/3272127.3275025","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T19:16:10Z","timestamp":1543432570000},"page":"1-10","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["Global-to-local generative model for 3D shapes"],"prefix":"10.1145","volume":"37","author":[{"given":"Hao","family":"Wang","sequence":"first","affiliation":[{"name":"Shenzhen University"}]},{"given":"Nadav","family":"Schor","sequence":"additional","affiliation":[{"name":"Tel Aviv University"}]},{"given":"Ruizhen","family":"Hu","sequence":"additional","affiliation":[{"name":"Shenzhen University"}]},{"given":"Haibin","family":"Huang","sequence":"additional","affiliation":[{"name":"Megvii \/ Face++ Research"}]},{"given":"Daniel","family":"Cohen-Or","sequence":"additional","affiliation":[{"name":"Shenzhen University and Tel Aviv University"}]},{"given":"Hui","family":"Huang","sequence":"additional","affiliation":[{"name":"Shenzhen University"}]}],"member":"320","published-online":{"date-parts":[[2018,12,4]]},"reference":[{"key":"e_1_2_2_1_1","volume-title":"Proc. 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Pointnet: Deep learning on point sets for 3D classification and segmentation . Proc. IEEE Conf. on Computer Vision & Pattern Recognition (2017), 652--660. Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3D classification and segmentation. Proc. IEEE Conf. on Computer Vision & Pattern Recognition (2017), 652--660."},{"key":"e_1_2_2_28_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015","unstructured":"Alec Radford , Luke Metz , and Soumith Chintala . 2015. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 ( 2015 ). Alec Radford, Luke Metz, and Soumith Chintala. 2015. 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