Affiliation:
1. Anhui University College of Art , Hefei , Anhui , , China .
Abstract
Abstract
The animation market is experiencing significant growth and diversification, with an expanding audience base and increasingly sophisticated aesthetic demands. This paper presents the development of a digital media art animation design system, leveraging four pivotal 3D technologies: mesh space deformation, 3D coordinate transformation, keyframe interpolation, and model collision detection. The system is enhanced with modules for scene editing, particle systems, and animation editing, which decompose the intricate special effects design of digital media art animations into manageable, editable components that offer real-time feedback. Furthermore, the system’s rendering capabilities and the animation design’s visual appeal were subjected to empirical evaluation. The findings indicate that within a 3D technology framework, the animation designs achieved average scores of 7.4371, 6.4720, and 7.0155 for recognizability, fidelity to the intended design, and overall impression, respectively, fulfilling the anticipated outcomes. Additionally, the animations produced are highly scalable and can be rendered swiftly and efficiently across diverse platforms. This study provides valuable insights for enhancing the effective deployment of 3D technologies in digital media art and animation design.
Reference17 articles.
1. Si, W., Qin, J., Chen, Z., Liao, X., Wang, Q., & Heng, P. A. (2018). Transactions on multimedia thin-feature-aware transport-velocity formulation for sph-based liquid animation. IEEE Transactions on Multimedia.
2. Gao, F. (2017). Research on 2d animation design based on depth image sequence. Revista de la Facultad de Ingenieria, 32(14), 490-495.
3. Tao, F., Sui, F., Liu, A., Qi, Q., Zhang, M., & Song, B., et al. (2019). Digital twin-driven product design framework. International Journal of Production Research, 57(11-12), 3935-3953.
4. Chunzhou, N.,& Yukai, Z. (2018). Design of unsupervised facial expression animation based on geometric grid measurement. International journal of reasoning-based intelligent systems.
5. Zhu, Y. (2022). Flattening of new media design based on deep reinforcement learning. Mobile information systems(Pt.5), 2022.