{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T17:04:08Z","timestamp":1782407048284,"version":"3.54.5"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>This paper presents a knowledge-guided generative adversarial network (KG-GAN) for culturally faithful virtual scene construction and immersive experience optimization. A domain knowledge graph (KG) encodes traditional cultural entities and relations as graph embeddings, which condition the generator together with textual prompts. The semantic consistency discriminator calculates text-image cosine similarity based on CLIP cross-modal embedding, jointly scoring lexical semantic consistency and symbol reconstruction accuracy. The generator employs AdaIN for knowledge-conditional modulation, and multi-scale training and PWC-Net optical flow regularization collaboratively optimize local details and inter-frame stability. To balance global realism and local cultural details., we adopt a multi-scale training schedule and an attention controller that dynamically allocates textures and color palettes to culture-critical regions. Temporal stability is further improved with a lightweight consistency loss.Experiments on a curated cultural corpus and human-corrected pairs report cultural semantic fidelity of 89.7%\u201392.1%, relation compliance of 86.9%\u201388.6%, inter-frame SSIM under fast transitions of 0.782, and optical-flow smoothness of 0.751. A vocational Chinese-language classroom case study shows higher cultural expressiveness, learner engagement, and motion comfort compared with diffusion-based and CLIP-guided baselines, with ablations confirming the contributions of KG constraints, the semantic discriminator, and attention control. The framework is plug-and-play for XR\/education\/museums, and we provide KG schemas, code, and evaluation scripts to support reproducible deployment.<\/jats:p>","DOI":"10.31449\/inf.v50i10.12258","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:44Z","timestamp":1773832364000},"source":"Crossref","is-referenced-by-count":1,"title":["KG-GAN: Knowledge Graph-Constrained GAN for Culturally Faithful Virtual Scene Synthesis"],"prefix":"10.31449","volume":"50","author":[{"given":"Peishu","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16141","published-online":{"date-parts":[[2026,3,18]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12258\/6596","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12258\/6596","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:12:45Z","timestamp":1773832365000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12258"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,18]]},"references-count":0,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2026,3,18]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i10.12258","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3,18]]}}}