Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Series Vol. 40 , 21 February 2024
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With the flourishing development of artificial intelligence and computer animation technologies, there has been an increasing intersection between these two. In the field of computer animation, the use of artificial intelligence significantly reduces the difficulties in design, production, and post-production processes, which has a massive impact on the entire field. The paper attempts to discuss the relationship between artificial intelligence and computer animation. Not only does the paper elaborate on the related applications of artificial intelligence in various subfields of computer animation, but it also analyzes existing problems and future development trends. The research indicates that AI has achieved significant breakthroughs in computer animation, such as auto-generation of animations, real-time character driving, and emotionally responsive animation creation. However, it also faces challenges like handling interactions in complex scenarios, maintaining realism, and animating high-level abstract concepts. Despite these challenges, it is believed that in the future, AI will further propel the development of computer animation, aiding creators in producing animations that are more vibrant, intricate, and personalized.
Computer Animation, Artificial Intelligence, Deep Learning, Neural Network
1. Holden, Daniel, et al. 2018. "Deep Learning Techniques for Animation." ACM Transactions on Graphics (TOG), vol. 37, no. 4, ACM.
2. Liu, Shihong, et al. 2018. "Data-driven Animation of Hand-contact Character-environment Interactions." Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation.
3. Wang, Xing, et al. 2018. "Characterizing Articulation in Expressive Robotic Faces." Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems.
4. Sheng, Jingwei, et al. 2020. "Animating Landscape: Self-supervised Learning of Decoupled Motion and Appearance for Single-image Video Synthesis." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.
5. Smith, Tony, et al. 2020. "Influence of AI on the Production Efficiency in Animation Industry." Journal of Computer Animation and Virtual Worlds, vol. 31, no. 3, Wiley.
6. Russell, Stuart J., and Peter Norvig. 2003. "Artificial intelligence: a modern approach."
7. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. 2012. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25, 1097-1105.
8. Zajac, E.E. 1964. A Computer-Animated Film. Bell System Technical Journal.
9. Lasseter, J. 1987. Principles of Traditional Animation Applied to 3D Computer Animation. ACM SIGGRAPH Computer Graphics.
10. Magnenat-Thalmann, N., & Thalmann, D. 2013. Handbook of Virtual Humans. Wiley.
11. Goodfellow, I., Bengio, Y., & Courville, A. 2016. Deep Learning. MIT Press.
12. Radford, A., Metz, L., & Chintala, S. 2015. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. arXiv.
13. Holden, D., Saito, J., Komura, T., & Joyce, T. 2017. Learning Motion Manifolds with Convolutional Autoencoders. ACM SIGGRAPH Asia 2017 Technical Briefs, 16.
14. Isla, D. 2005. Handling Complexity in the Halo 2 AI. Gamasutra.
15. Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., … & Petersen, S. 2015. Human-level control through deep reinforcement learning. Nature, 518(7540), 529-533.
16. Ozair, S., … & Bengio, Y. 2014. Generative adversarial nets. Advances in neural information processing systems, 27, 2672-2680.
17. Zheng, Z., Zhang, Y., Yu, X., Zhou, K., & Guo, B. 2020. DeepPRT: Pose-robust neural texture rendering from a single image. ACM Transactions on Graphics (TOG), 39(4), 1-12.
18. Chaitanya, C. R. A., Kaplanyan, A. S., Schied, C., Salvi, M., Lefohn, A., Nowrouzezahrai, D., & Aila, T. 2017. Interactive reconstruction of Monte Carlo image sequences using a recurrent denoising autoencoder. ACM Transactions on Graphics (TOG), 36(4), 1-12.
19. Thuerey, N., Pfaff, T., & Koltun, V. 2018. Deep Fluids: A Generative Network for Parameterized Fluid Simulations. Computer Graphics Forum, 37(2), 59-70.
20. Arevian, A.C., Kapoor, A. 2018. Automating Video Editing with Artificial Intelligence. IEEE Spectrum.
21. Thalmann, D. 2013. Handbook of Virtual Humans. Wiley.
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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