{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T15:46:59Z","timestamp":1776786419819,"version":"3.51.2"},"reference-count":40,"publisher":"Association for Computing Machinery (ACM)","issue":"5","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62202280"],"award-info":[{"award-number":["62202280"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Taishan Scholar Project of Shandong Province","award":["tsqn202312196"],"award-info":[{"award-number":["tsqn202312196"]}]},{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2024QF034, ZR2021QF017"],"award-info":[{"award-number":["ZR2024QF034, ZR2021QF017"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2026,5,31]]},"abstract":"<jats:p>\n                    Generative 3D human head reconstruction in\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(360^{\\circ}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    is attracting increasing attention because of its flexibility in downstream animation applications. Existing generative 3D head synthesis approaches are primarily limited to near-frontal face priors, which cause distorted artifacts at large view angles. In this article, we introduce a novel Pseudo-Supervision Guided Spatial Optimization (PSO-HEAD) framework that reconstructs 3D view-consistent full-head through explicitly introducing pseudo-label of back-head supervision for spatial texture and geometric optimization. Particularly, our PSO-HEAD introduces two key improvements, i.e., Pseudo-Supervision Augmented Inversion (PSA-Inversion) and Full-Head Aware Generative Enhancement (FAGE). PSA-Inversion augments plausible invisible back-head as pseudo-supervision to optimize the view-hallucinated latent code conditioned on the augmented camera poses via GAN inversion, enforcing 3D spatial consistency across both visible and invisible regions. Furthermore, FAGE fine-tunes the 3D GAN on a proposed auxiliary FK-Enhance dataset deriving from either generated or real-world high-quality back-head images, which therefore improves the generalization of our PSO-HEAD to diverse hairstyles or underrepresented regions. Benefiting from the improvements, our PSO-HEAD enables efficient\n                    <jats:inline-formula content-type=\"math\/tex\">\n                      <jats:tex-math notation=\"LaTeX\" version=\"MathJax\">\\(360^{\\circ}\\)<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    view-consistent full-head generation from single input images, particularly improving reconstruction fidelity of unobserved regions, which quantitatively and qualitatively outperforms the state-of-the-art methods.\n                  <\/jats:p>","DOI":"10.1145\/3801549","type":"journal-article","created":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T10:53:01Z","timestamp":1773399181000},"page":"1-19","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["PSO-HEAD: Pseudo-Supervision Guided Spatial Optimization for View-Consistent 3D Full-Head Reconstruction"],"prefix":"10.1145","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6794-7352","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0576-4944","authenticated-orcid":false,"given":"Yiheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7303-5712","authenticated-orcid":false,"given":"Xiaolin","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9491-5560","authenticated-orcid":false,"given":"Chongxin","family":"Liang","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2131-1671","authenticated-orcid":false,"given":"Caifeng","family":"Shan","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, China and School of Intelligence Science and Technology, Nanjing University, Nanjing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-0334-3681","authenticated-orcid":false,"given":"Haojie","family":"Li","sequence":"additional","affiliation":[{"name":"College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,4,21]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.02011"},{"key":"e_1_3_1_3_2","first-page":"896","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yuan Ye","year":"2024","unstructured":"Ye Yuan, Xueting Li, Yangyi Huang, Shalini De Mello, Koki Nagano, Jan Kautz, and Umar Iqbal. 2024. 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