{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/paired-3d-model-generation-with-conditional","title":"Paired 3D Model Generation with Conditional Generative Adversarial Networks","arxiv_id":"1808.03082","date":"2018-08-09","proceeding":null,"authors":["Cihan Öngün","Alptekin Temizel"],"abstract":"Generative Adversarial Networks (GANs) are shown to be successful at\ngenerating new and realistic samples including 3D object models. Conditional\nGAN, a variant of GANs, allows generating samples in given conditions. However,\nobjects generated for each condition are different and it does not allow\ngeneration of the same object in different conditions. In this paper, we first\nadapt conditional GAN, which is originally designed for 2D image generation, to\nthe problem of generating 3D models in different rotations. We then propose a\nnew approach to guide the network to generate the same 3D sample in different\nand controllable rotation angles (sample pairs). Unlike previous studies, the\nproposed method does not require modification of the standard conditional GAN\narchitecture and it can be integrated into the training step of any conditional\nGAN. Experimental results and visual comparison of 3D models show that the\nproposed method is successful at generating model pairs in different\nconditions.","url_abs":"http://arxiv.org/abs/1808.03082v2","url_pdf":"http://arxiv.org/pdf/1808.03082v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"paired-3d-model-generation-with-conditional","repo_url":"https://github.com/cihanongun/3D-CGAN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}