{"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/panohead-geometry-aware-3d-full-head","title":"PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\\circ}$","arxiv_id":"2303.13071","date":"2023-03-23","proceeding":null,"authors":["Sizhe An","Hongyi Xu","Yichun Shi","Guoxian Song","Umit Ogras","Linjie Luo"],"abstract":"Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. At its core, we lift up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, we propose a novel two-stage self-adaptive image alignment for robust 3D GAN training. We further introduce a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. Our method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds. Benefiting from these designs, our method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, we show that our system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars.","url_abs":"https://arxiv.org/abs/2303.13071v1","url_pdf":"https://arxiv.org/pdf/2303.13071v1.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":"panohead-geometry-aware-3d-full-head","repo_url":"https://github.com/sizhean/panohead","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"image-generation","task_name":"Image Generation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.13071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13071"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/sizhean/panohead","reach":null}],"summary":{"ran_draft_wrong":1,"ran_fixture":2,"ran_honours":3},"by_repo_kind":{"listed":{"samples":6,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"eae3d419e3bac1bb","entry":"create_samples","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_videos.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_videos.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"eae3d419e3bac1bb"}},{"code_sha256_prefix":"456c172851946697","entry":"layout_grid","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_videos.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_videos.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"456c172851946697"}},{"code_sha256_prefix":"f47dff4b7b27bba7","entry":"make_transform","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_samples.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_samples.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"well_formed","behaviour_fingerprint":true,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f47dff4b7b27bba7"}},{"code_sha256_prefix":"236de8614a178382","entry":"parse_range","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_samples.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_samples.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"236de8614a178382"}},{"code_sha256_prefix":"025e4c7cda618f25","entry":"parse_range","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_videos.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_videos.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"025e4c7cda618f25"}},{"code_sha256_prefix":"180016b9227381b1","entry":"parse_vec2","repo":"sizhean/panohead","repo_kind":"listed","path":"gen_samples.py","file_url":"https://github.com/sizhean/panohead/blob/HEAD/gen_samples.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"CC0-1.0","inline_ok":true,"mcp_get_code":{"code_sha256":"180016b9227381b1"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}