SEH-ConGAN: A Scalable GAN-based Framework for Robot-Assisted Automation in Animation Production

Abstract

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’s 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.