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References

1 
Alonso G. E., Jin X. G., 2022, Skeleton-level control for multi-agent simulation through deep reinforcement learning, Computer Animation and Virtual Worlds, Vol. 33, No. 3-4, pp. 11DOI
2 
Aylagas M. V., Leon H. A., Teye M., Tollmar K., 2022, Voice2Face: Audio-driven facial and tongue rig animations with cVAEs, Computer Graphics Forum, Vol. 41, No. 8, pp. 255-265DOI
3 
Bertiche H., Madadi M., Escalera S., 2021, PBNS: Physically based neural simulation for unsupervised garment pose space deformation, ACM Transactions on Graphics, Vol. 40, No. 6, pp. 14DOI
4 
Chen J., Fan C., Zhang Z., Li G., Zhao Z., Deng Z., 2023, A music-driven deep generative adversarial model for Guzheng playing animation, Ieee Transactions on Visualization and Computer Graphics, Vol. 29, No. 2, pp. 1400-1414DOI
5 
Chen S. C., Liu G. T., Wong S. K., 2021, Generation of multiagent animation for object transportation using deep reinforcement learning and blend-trees, Computer Animation and Virtual Worlds, Vol. 32, No. 3-4, pp. 10DOI
6 
Choi Y., Seo B., Kang S., Choi J., 2023, Study on 2D Sprite*3.Generation using the impersonator network, KSII Transactions on Internet and Information Systems, Vol. 17, No. 7, pp. 1794-1806DOI
7 
Ding W., Li W. F., 2023, High speed and accuracy of animation 3D pose recognition based on an improved deep convolution neural network, Applied Sciences-Basel, Vol. 13, No. 13, pp. 7566DOI
8 
Duan L., 2022, Application of animation character intelligent analysis algorithm based on deep learning, Mobile Information Systems, Vol. 2022DOI
9 
He C., Jia Y., 2023, Automatic depth estimation and background blurring of animated scenes based on deep learning, Traitement Du Signal, Vol. 40, No. 5, pp. 2225-2232DOI
10 
Hu Z. P., Wu R., Li L., Zhang R., Hu Y., Qiu F., 2023, Deep learning applications in games: A survey from a data perspective, Applied Intelligence, Vol. 53, No. 24, pp. 31106-31128DOI
11 
Kwiatkowski A., Alvarado E., Kaiogeiton V., Liu C. K., Pettré J., van de Panne M., Cani M.-P., 2022, A survey on reinforcement learning methods in character animation, Computer Graphics Forum, Vol. 41, No. 2, pp. 613-639DOI
12 
Li C., Lussell L., Komura T., 2021, Multi-agent reinforcement learning for character control, Visual Computer, Vol. 37, No. 12, pp. 3115-3123DOI
13 
Li R., Shi R., Kanai T., 2023, Detail-aware deep clothing animations infused with multi-source attributes, Computer Graphics Forum, Vol. 42, No. 1, pp. 231-244DOI
14 
Liu G. T., Wong S. K., 2024, Mastering broom-like tools for object transportation animation using deep reinforcement learning, Computer Animation and Virtual Worlds, Vol. 35, No. 3, pp. 15DOI
15 
Liu L. J., Zheng Y. Y., Tang D., Yuan Y., Fan C. J., Zhou K., 2019, NeuroSkinning: Automatic skin binding for production characters with deep graph networks, ACM Transactions on Graphics, Vol. 38, No. 4, pp. 114DOI
16 
Luo Y. S., Soeseno J. H., Chen T. P. C., Chen W. C., 2020, CARL: Controllable agent with reinforcement learning for quadruped locomotion, ACM Transactions on Graphics, Vol. 39, No. 4, pp. 38DOI
17 
Morace C. C., Le T. N. H., Yao S. Y., Zhang S. W., Lee T. Y., 2022, Learning a perceptual manifold with deep features for animation video resequencing, Multimedia Tools and Applications, Vol. 81, No. 17, pp. 23687-23707DOI
18 
Moutafidou A., Toulatzis V., Fudos I., 2024, Deep fusible skinning of animation sequences, Visual Computer, Vol. 40, No. 8, pp. 5695-5715DOI
19 
Paier W., Hilsmann A., Eisert P., 2020, Interactive facial animation with deep neural networks, IET Computer Vision, Vol. 14, No. 6, pp. 359-369DOI
20 
Park S., Ryu H., Lee S., Lee S., Lee J., 2019, Learning predict-and-simulate policies from unorganized human motion data, ACM Transactions on Graphics, Vol. 38, No. 6, pp. 205DOI
21 
Peng T., Kuamg J., Liang J., Hu X., Miao J., P. Zhu , L. Li , F. Yu , M. Jiang , 2023, GSNet: Generating 3D garment animation via graph skinning network, Graphical Models, Vol. 129, pp. 10DOI
22 
Qiao Z., Li T. X., Hui L., Liu R. J., 2023, A deep learning-based framework for fast generation of photorealistic hair animations, IET Image Processing, Vol. 17, No. 2, pp. 375-387DOI
23 
Shan F., Wang Y. Y., 2022, Animation design based on 3D visual communication technology, Scientific Programming, Vol. 2022DOI
24 
Tan J., Tian Y., 2023, Fuzzy retrieval algorithm for film and television animation resource database based on deep neural network, Journal of Radiation Research and Applied Sciences, Vol. 16, No. 4, pp. 100675DOI
25 
Ullah S., Ijjeh A. A., Kudela P., 2023, Deep learning approach for delamination identification using animation of Lamb waves, Engineering Applications of Artificial Intelligence, Vol. 117, pp. 105520DOI
26 
Wu P., Chen S. J., 2022, A study on the relationship between painter's psychology and anime creation style based on a deep neural network, Computational Intelligence and Neuroscience, Vol. 2022DOI
27 
Yu C., Wang W. M., Yan J. H., 2020, Self-supervised animation synthesis through adversarial training, IEEE Access, Vol. 8, pp. 128140-128151DOI
28 
Yu Z. X., Wang H. H., Ren J., 2022, RealPRNet: A real-time phoneme-recognized network for "believable" speech animation, IEEE Internet of Things Journal, Vol. 9, No. 7, pp. 5357-5367DOI
29 
Zhang Y. L., Ban X. J., Du F. L., Di W., 2020, FluidsNet: End-to-end learning for Lagrangian fluid simulation, Expert Systems with Applications, Vol. 152, pp. 113410DOI
30 
Zhang Z. N., Wu Y. H., Pan Z. G., Li W. Q., Su Y., 2022, A novel animation authoring framework for the virtual teacher performing experiment in mixed reality, Computer Applications in Engineering Education, Vol. 30, No. 2, pp. 550-563DOI