Character Design For 3d Animation
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Research on 3D animation character design based on multimedia interaction
Multimedia Tools and Applications (2019)Cite this article
Abstract
Aiming at the low intelligence, low interaction performance and low efficiency of traditional 3D animation characters in multimedia interaction, this paper designs a multimedia scene model and a 3D animation role agent based on the multimedia interaction model of Computer Supported Cooperative work, and designs a multimedia scene model and a 3D animation role agent through 3DS Max. In order to make Agent more intelligent in multimedia interaction, a deep Q-Learning neural network model is introduced in this paper. Through this model, the reinforcement learning of 3D animation role agent in multimedia interaction scenarios is introduced. Taking basketball games as an example, multimedia interactive scenes and 3D animation role agent are constructed by using 3DS Max software. In the subsequent comparative experiments, it is proved that the deep Q-Learning neural network model constructed in this paper is more suitable for 3D animation role agent in multimedia interactive environment. It makes 3D animation characters more suitable for multimedia interaction, and builds higher performance and efficiency of intelligent interactive action.
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References
- 1.
Bessmeltsev M, Chang W, Vining N et al (2015) Modeling character canvases from cartoon drawings[J]. ACM Transactions on Graphics (TOG) 34(5):162
Article Google Scholar
- 2.
Caicedo JC, Lazebnik S (2015) Active object localization with deep reinforcement learning[C]//Proceedings of the IEEE International Conference on Computer Vision. 2488–2496
- 3.
Darlington J (2018) Techno-Wizardry and movie magic: the trace of labour (or lack thereof) in 3D digital animation[J]. Information, Communication & Society, 1–17
- 4.
Duan Y, Chen X, Houthooft R, et al. (2016) Benchmarking deep reinforcement learning for continuous control[C]//International Conference on Machine Learning. 1329–1338
- 5.
Gao Q, Gu H, Zhao P et al (2018) Fabrication of electrospun nanofibrous scaffolds with 3D controllable geometric shapes[J]. Mater Des 157:159–169
Article Google Scholar
- 6.
Gunanto SG, Hariadi M, Yuniarno EM (2016) Generating weight paint area on 3D cartoon-face models[J]. International Information Institute (Tokyo). Information 19(9B):4183
Google Scholar
- 7.
Gunanto SG, Hariadi M, Yuniarto EM (2016) Improved 3D face feature-point nearest neighbor clustering using orthogonal face map[J]. Adv Sci Lett 22(8):1882–1886
Article Google Scholar
- 8.
Kulkarni TD, Narasimhan K, Saeedi A, et al. (2016) Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation[C]//Advances in neural information processing systems. 3675–3683
- 9.
Liu Y, Wang W. (2016) Body-structure-based cartoon character modeling from multi-view hand-drawings[C]//Computer Science and Network Technology (ICCSNT), 2016 5th International Conference on. IEEE, 89–93
- 10.
Mnih V, Kavukcuoglu K, Silver D et al (2015) Human-level control through deep reinforcement learning[J]. Nature 518(7540):529
Article Google Scholar
- 11.
Mnih V, Badia A P, Mirza M, et al. (2016) Asynchronous methods for deep reinforcement learning[C]//International conference on machine learning. 1928–1937
- 12.
Plattner N, Noé F (2015) Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models[J]. Nat Commun 6:7653
Article Google Scholar
- 13.
Van Hasselt H, Guez A, Silver D (2016) Deep Reinforcement Learning with Double Q-Learning[C]//AAAI 2:5
Google Scholar
- 14.
Xie C, Zhang Z, Wang C et al (2018) Object tracking method based on 3D cartoon animation in broadcast soccer videos[J]. International Journal of Performability Engineering 14(8):1774
Google Scholar
- 15.
Zhou J, Wu HT, Liu Z, et al. (2018) 3D cartoon face rigging from sparse examples[J]. The Visual Computer, 1–11
- 16.
Zhu Y, Mottaghi R, Kolve E, et al. (2017) Target-driven visual navigation in indoor scenes using deep reinforcement learning[C]//Robotics and Automation (ICRA), 2017 IEEE International Conference on. IEEE, 3357–3364
Download references
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Hu, P., Wen, J. Research on 3D animation character design based on multimedia interaction. Multimed Tools Appl (2019). https://doi.org/10.1007/s11042-019-7538-z
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DOI : https://doi.org/10.1007/s11042-019-7538-z
Keywords
- Multimedia interaction
- Collaborative work
- 3D animation characters
- Deep Q-learning neural network
Character Design For 3d Animation
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