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The Science and Information (SAI) Organization publishes open-access peer-reviewed journals in computer science and artificial intelligence.

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Research Article | Open Access |

Artificial Intelligence-Driven Physical Simulation and Animation Generation in Computer Graphics

Author 1: Fei Wang
International Journal of Advanced Computer Science and Applications (IJACSA) · Vol. 16, No. 5 · Published 2025

DOI: https://doi.org/10.14569/IJACSA.2025.0160568

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Abstract

This study explores an expression synthesis algorithm anchored in Generative Adversarial Networks (GAN) with attention mechanisms, achieving enhanced authenticity in facial expression generation. Evaluated on the MUG and Oulu-CASIA datasets, our method synthesizes six expressions with superior clarity (96.63±0.26 confidence for neutral expressions) and smoothness (SSIM >0.92 for video frames), outperforming StarGAN and ExprGAN in detail preservation and temporal stability. The proposed model demonstrates significant advantages in realism and identity retention, validated through quantitative metrics and comparative experiments.

Keywords

GAN computer graphics expression synthesis animation generation

How to Cite this Article

Wang, F. (2025). Artificial Intelligence-Driven Physical Simulation and Animation Generation in Computer Graphics. International Journal of Advanced Computer Science and Applications, 16(5). https://doi.org/10.14569/IJACSA.2025.0160568

Wang, Fei. "Artificial Intelligence-Driven Physical Simulation and Animation Generation in Computer Graphics." International Journal of Advanced Computer Science and Applications, vol. 16, no. 5, 2025, https://doi.org/10.14569/IJACSA.2025.0160568.

@article{Wang2025,
  title     = {Artificial Intelligence-Driven Physical Simulation and Animation Generation in Computer Graphics},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Fei Wang},
  doi       = {10.14569/IJACSA.2025.0160568},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160568}
}

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