{"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/cips-3d-a-3d-aware-generator-of-gans-based-on","title":"CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis","arxiv_id":"2110.09788","date":"2021-10-19","proceeding":null,"authors":["Peng Zhou","Lingxi Xie","Bingbing Ni","Qi Tian"],"abstract":"The style-based GAN (StyleGAN) architecture achieved state-of-the-art results for generating high-quality images, but it lacks explicit and precise control over camera poses. The recently proposed NeRF-based GANs made great progress towards 3D-aware generators, but they are unable to generate high-quality images yet. This paper presents CIPS-3D, a style-based, 3D-aware generator that is composed of a shallow NeRF network and a deep implicit neural representation (INR) network. The generator synthesizes each pixel value independently without any spatial convolution or upsampling operation. In addition, we diagnose the problem of mirror symmetry that implies a suboptimal solution and solve it by introducing an auxiliary discriminator. Trained on raw, single-view images, CIPS-3D sets new records for 3D-aware image synthesis with an impressive FID of 6.97 for images at the $256\\times256$ resolution on FFHQ. We also demonstrate several interesting directions for CIPS-3D such as transfer learning and 3D-aware face stylization. The synthesis results are best viewed as videos, so we recommend the readers to check our github project at https://github.com/PeterouZh/CIPS-3D","url_abs":"https://arxiv.org/abs/2110.09788v1","url_pdf":"https://arxiv.org/pdf/2110.09788v1.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":"cips-3d-a-3d-aware-generator-of-gans-based-on","repo_url":"https://github.com/PeterouZh/CIPS-3D","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-aware-image-synthesis","task_name":"3D-Aware Image Synthesis"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-aware-image-synthesis-on-ffhq-256-x-256","task":"3D-Aware Image Synthesis","dataset":"FFHQ 256 x 256","model":"CIPS-3D","rank_in_archive_order":1,"of":4,"metrics":{"FID":"6.97","KID":"2.87"},"uses_additional_data":false},{"leaderboard":"/sota/3d-aware-image-synthesis-on-ffhq-256-x-256","task":"3D-Aware Image Synthesis","dataset":"FFHQ 256 x 256","model":"StyleNeRF","rank_in_archive_order":2,"of":4,"metrics":{"FID":"8.00","KID":"3.70"},"uses_additional_data":false},{"leaderboard":"/sota/3d-aware-image-synthesis-on-ffhq-256-x-256","task":"3D-Aware Image Synthesis","dataset":"FFHQ 256 x 256","model":"pi-GAN","rank_in_archive_order":3,"of":4,"metrics":{"FID":"34.56","KID":" 26.58"},"uses_additional_data":false},{"leaderboard":"/sota/3d-aware-image-synthesis-on-ffhq-256-x-256","task":"3D-Aware Image Synthesis","dataset":"FFHQ 256 x 256","model":"GIRAFFE","rank_in_archive_order":4,"of":4,"metrics":{"FID":"63.33","KID":"50.94"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2110.09788","atlas_url":"https://app.syntology.ai/?focus=2110.09788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.09788"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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