{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T21:59:05Z","timestamp":1770069545822,"version":"3.49.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>The animation production process is traditionally labor-intensive, requiring extensive manual effort in character motion design, scene composition, and post-production editing. To overcome these limitations, this research introduces a robot-assisted automation system integrated with artificial intelligence (AI) to streamline and accelerate animation development. The system incorporates a motion capture interface for acquiring human movement data, a feedback-enabled robotic arm to replicate and analyze motions, and a simulation environment for virtual testing. Preprocessing includes missing-value handling and Z-score normalization, after which structured motion sequences (3D joint coordinates, robotic servo positions, and torque data) are provided as input to the Scalable Elephant Herding-tuned Conditional Generative Adversarial Network (SEH-ConGAN). The model generates refined outputs such as smooth motion trajectories, facial expression synthesis, and context-aware style transfer. Statistical analysis using a paired t-test, 95% confidence intervals, and Cohen\u2019s d effect size was performed to confirm the significant performance improvement of SEH-ConGAN over baseline models Performance is evaluated using 5-fold cross-validation and achieves an accuracy of 0.96, precision of 0.97, recall of 0.96, and F1-score of 0.96. Comparative analysis of motion generation metrics shows that SEH-ConGAN surpasses existing models achieving the best MPJPE (16.7), FID (11.3), Smoothness (0.028), and Diversity (0.72), demonstrating superior motion accuracy, trajectory smoothness, and animation realism. . The findings demonstrate that combining robotics with SEH-ConGAN provides a scalable solution for producing high-quality animations with reduced time, cost, and manual intervention.<\/jats:p>","DOI":"10.31449\/inf.v50i5.10853","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:32Z","timestamp":1770027932000},"source":"Crossref","is-referenced-by-count":0,"title":["SEH-ConGAN: A Scalable GAN-based Framework for Robot-Assisted Automation in Animation Production"],"prefix":"10.31449","volume":"50","author":[{"given":"Hongping","family":"Tang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,2]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10853\/6430","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/10853\/6430","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:32Z","timestamp":1770027932000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/10853"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,2,2]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i5.10853","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,2]]}}}