The application of body symbols in contemporary art from the perspective of Da Vinci’s theory

[1] Wen, Y. H., Gao, L., Fu, H., Zhang, F. L., et al. (2019). Graph CNNs with motif and variable temporal block for skeleton-based action recognition. In Proceedings of the AAAI conference on artificial intelligence 33(01), 8989-8996. Search in Google Scholar

[2] Xia, S. H., Gao, L., Lai, Y. K., et al. (2017). A Survey on Human Performance Capture and Animation. Journal of Computer Science and Technology, 32(3), 536-554. Search in Google Scholar

[3] Fragkiadaki, K., Levine, S., Felsen, P., et al. (2015). Recurrent network models for human dynamics// Proceedings of the IEEE international conference on computer vision. 4346-4354. Search in Google Scholar

[4] Jain, A., Zamir, A. R., Savarese, S., et al. (2016). Structural-rnn: Deep learning on spatio-temporal graphs// Proceedings of the ieee conference on computer vision and pattern recognition. 5308-5317. Search in Google Scholar

[5] Martinez, J., Black, M. J., Romero, J. (2017). On human motion prediction using recurrent neural networks//Proceedings of the IEEE conference on computer vision and pattern recognition. 2891-2900. Search in Google Scholar

[6] Barsoum, E., Kender, J., Liu, Z. (2018). Hp-gan: Probabilistic 3d human motion prediction via gan// Proceedings of the IEEE conference oncomputer vision and pattern recognition workshops. 1418-1427. Search in Google Scholar

[7] Shu, X., Zhang, L., Sun, Y., Tang, J. (2020). “Host-parasite: Graph LSTM-in-LSTM for group activity recognition”, IEEE Trans. Neural Netw. Learn. Syst. Search in Google Scholar

[8] Zhou, Y., Li, Z., Xiao, S., et al. (2018). Auto-Conditioned Recurrent Networks for Extended Complex Human Motion Synthesis// Proceedings of the International Conference on Learning Representations. Search in Google Scholar

[9] Ionescu, C., Papava, D., Olaru, V., et al. (2013). Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence, 36(7), 1325-1339. Search in Google Scholar

[10] Harvey, F. G., Roy, J., Kanaa, D., et al. (2018). Recurrent semi-supervised classification and constrained adversarial generation with motion capture data. Image and Vision Computing, 78, 42-52. Search in Google Scholar

[11] Habibie, I., Holden, D., Schwarz, J., et al. (2017). A recurrent variational autoencoder for human motion synthesis // Proceedings of the 28th British Machine Vision Conference. Search in Google Scholar

[12] Lee, K., Lee, S., Lee, J., et al. (2018). Interactive character animation by learning multi-objective control. ACM Transactions on Graphics (TOG), 37(6), 1-10. Search in Google Scholar

[13] Wang, Z., Chai, J., Xia, S., et al. (2019). Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control. IEEE transactions on visualization and computer graphics, 27(1), 14-28. Search in Google Scholar

[14] Holden, D., Saito, J., Komura, T., et al. (2016). A deep learning framework for character motion synthesis and editing. ACM Transactions on Graphics (TOG), 35(4), 1-11. Search in Google Scholar

[15] Holden, D., Komura, T., Saito, J. (2017). Phase-functioned neural networks for character control. ACM Transactions on Graphics (TOG), 36(4), 1-13. Search in Google Scholar

[16] Zhang, H., Starke, S., Komura, T., et al. (2018). Mode-Adaptive Neural Networks for Quadruped Motion Control. ACM Transactions on Graphics (TOG), 37(4), 1-11. Search in Google Scholar

[17] Graves, A. (2013). Generating Sequences With Recurrent Neural Networks[EB/OL]. arXiv preprint arXiv: 1308.0850. Search in Google Scholar

[18] Werbos, P. J. (1990). Backpropagation through time: what it does and how to do it. Proceedings of the IEEE, 78(10), 1550-1560. Search in Google Scholar

[19] Martinez, J., Black M. J., Romero, J. (2017). “On human motion prediction using recurrent neural networks”, Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 4674-4683. Search in Google Scholar

[20] Yan X., et al., (2018). “MT-VAE: Learning motion transformations to generate multimodal human dynamics”, Proc. Eur. Conf. Comput. Vis., pp. 276-293. Search in Google Scholar

[21] Yan, R., Tang, J., Shu, X., Li Z. (2018). “Participation-contributed temporal dynamic model for group activity recognition”, Proc. ACM Int. Conf. Multimedia, pp. 1292-1300. Search in Google Scholar

[22] Tang, Y., Ma, L., Liu, W. (2018). “Long-term human motion prediction by modeling motion context and enhancing motion dynamic”, Proc. Int. Joint Conf. Artif. Intell., pp. 935-941. Search in Google Scholar

[23] Liu, J., Shahroudy, A., et al. (2018). “Skeleton-based action recognition using spatio-temporal LSTM network with trust gates”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 12, pp. 3007-3021. Search in Google Scholar

[24] Shu, X., Tang, J., Qi, G. (2019). “Hierarchical long short-term concurrent memory for human interaction recognition”, IEEE Trans. Pattern Anal. Mach. Intell. Search in Google Scholar

[25] Tang, J., Shu, X. (2019). “Coherence constrained graph LSTM for group activity recognition”, IEEE Trans. Pattern Anal. Mach. Intell. Search in Google Scholar

[26] Fang Chunhua, Tao Yuning, Wang Jianguo, You Haixin, Cui Yan & Zhou Mi. (2021). Research on Leakage Current Waveform Spectrum Characteristics of Artificial Pollution Porcelain Insulator. Frontiers in Energy Research. https://doi.org/10.3389/FENRG.2021.798048. Search in Google Scholar

[27] Pavllo, D., Grangier, D., and Auli, M., (2018). “Quaternet: A quaternion-based recurrent model for human motion”, Proc. British Mach. Vis. Conf., pp. 1-14. Search in Google Scholar

[28] Li, C., Zhang, Z., Lee, W, S. (2018). “Convolutional sequence to sequence model for human dynamics”, Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 5226-5234. Search in Google Scholar

[29] Gui, L., Wang, Y. (2018). “Adversarial geometry-aware human motion prediction”, Proc. Eur. Conf. Comput. Vis., pp. 786-803. Search in Google Scholar

[30] Zhao, R., and Ji, Q. (2018). “An adversarial hierarchical hidden markov model for human pose modeling and generation”, Proc. AAAI Conf. Artif. Intell., pp. 2636-2643, 2018. Search in Google Scholar

[31] Zhao, R., Su, H., and Ji, Q. (2020). “Bayesian adversarial human motion synthesis”, Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp. 6224-6233. Search in Google Scholar

[32] Varol, G., Laptev, I., and Schmid, C. (2018). “Long-term temporal convolutions for action recognition”, IEEE Trans. Pattern Anal. Mach. Intell., vol. 40, no. 6, pp. 1510-1517. Search in Google Scholar

[33] Yan, S., Xiong, Y., Lin, D. (2018). “Spatial temporal graph convolutional networks for skeleton-based action recognition”, Proc. AAAI Conf. Artif. Intell., pp. 7444-7452. Search in Google Scholar

[34] Fang Chunhua, Tao Yuning, Wang Jianguo, Ding Can, Huang Li, Zhou Mi... & Wang Yali. (2021). Mapping Relation of Leakage Currents of Polluted Insulators and Discharge Arc Area. Frontiers in Energy Research. https://doi.org/10.3389/FENRG.2021.777230. Search in Google Scholar

[35] Hou, Y., Li, Z., Wang, P., and Li, W., “Skeleton optical spectra-based action recognition using convolutional neural networks”, IEEE Trans. Circuits Syst. Video Technol., vol. 28, no. 3, pp. 807-811. Search in Google Scholar