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

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Publicado en:International Journal of Advanced Computer Science and Applications vol. 16, no. 5 (2025)
Autor principal: PDF
Publicado:
Science and Information (SAI) Organization Limited
Materias:
Smoothness
Animation
Artificial intelligence
Physical simulation
Computer graphics
Generative adversarial networks
Deep learning
Computer science
Realism
Neural networks
Quantitative analysis
Acceso en línea:Citation/Abstract
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Resumen: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.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2025.0160568
Fuente:Advanced Technologies & Aerospace Database