{"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/class-continuous-conditional-generative","title":"Class-Continuous Conditional Generative Neural Radiance Field","arxiv_id":"2301.00950","date":"2023-01-03","proceeding":null,"authors":["Jiwook Kim","Minhyeok Lee"],"abstract":"The 3D-aware image synthesis focuses on conserving spatial consistency besides generating high-resolution images with fine details. Recently, Neural Radiance Field (NeRF) has been introduced for synthesizing novel views with low computational cost and superior performance. While several works investigate a generative NeRF and show remarkable achievement, they cannot handle conditional and continuous feature manipulation in the generation procedure. In this work, we introduce a novel model, called Class-Continuous Conditional Generative NeRF ($\\text{C}^{3}$G-NeRF), which can synthesize conditionally manipulated photorealistic 3D-consistent images by projecting conditional features to the generator and the discriminator. The proposed $\\text{C}^{3}$G-NeRF is evaluated with three image datasets, AFHQ, CelebA, and Cars. As a result, our model shows strong 3D-consistency with fine details and smooth interpolation in conditional feature manipulation. For instance, $\\text{C}^{3}$G-NeRF exhibits a Fr\\'echet Inception Distance (FID) of 7.64 in 3D-aware face image synthesis with a $\\text{128}^{2}$ resolution. Additionally, we provide FIDs of generated 3D-aware images of each class of the datasets as it is possible to synthesize class-conditional images with $\\text{C}^{3}$G-NeRF.","url_abs":"https://arxiv.org/abs/2301.00950v3","url_pdf":"https://arxiv.org/pdf/2301.00950v3.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":"class-continuous-conditional-generative","repo_url":"https://github.com/tom919654/C3G-NeRF","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-aware-image-synthesis","task_name":"3D-Aware Image Synthesis"},{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-to-image-translation","task_name":"Image-to-Image Translation"},{"task_slug":"nerf","task_name":"NeRF"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/image-generation-on-celeba-128x128","task":"Image Generation","dataset":"CelebA 128x128","model":"C^3G-NeRF","rank_in_archive_order":2,"of":5,"metrics":{"FID":"7.64"},"uses_additional_data":false},{"leaderboard":"/sota/image-generation-on-celeba-64x64","task":"Image Generation","dataset":"CelebA 64x64","model":"C^3G-NeRF","rank_in_archive_order":22,"of":39,"metrics":{"FID":"5.6"},"uses_additional_data":false},{"leaderboard":"/sota/image-to-image-translation-on-afhq","task":"Image-to-Image Translation","dataset":"AFHQ","model":"","rank_in_archive_order":2,"of":2,"metrics":{"LPIPS":"0.122"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}