[1] Kong Y, Fu Y. Human action recognition and prediction: a survey [OL]. 2018[2022-06-01]. https://arxiv.org/pdf/1806.11230.pdf. [2] 杨天明, 陈志, 岳文静. 基于视频深度学习的时空双流人物动作识别模型[J]. 计算机应用, 2018, 38(3): 895-899, 915. Yang T M, Chen Z, Yue W J. A spatiotemporal dual-stream human action recognition model based on video deep learning [J]. Computer Applications, 2018, 38(3): 895-899, 915. (in Chinese) [3] 马翠红, 王毅, 毛志强. 基于注意力的双流CNN的行为识别[J]. 计算机工程与设计, 2020, 41(10): 2903-2906. Ma C H, Wang Y, Mao Z Q. Action recognition based on attention-based dual-stream CNN [J]. Computer Engineering and Design, 2020, 41(10): 2903-2906. (in Chinese) [4] 宋立飞, 翁理国, 汪凌峰, 等. 多尺度输入3D卷积融合双流模型的行为识别方法[J]. 计算机辅助设计与图形学学报, 2018, 30(11): 2074-2083. Song L F, Weng L G, Wang L F, et al. Behavior recognition method based on multiscale input 3D convolution fusion two-stream model [J]. Journal of Computer Aided Design and Graphics, 2018, 30(11): 2074-2083. (in Chinese) [5] Zhou Y, Sun X, Luo C, et al. Spatio-temporal fusion in 3D CNNs: a probabilistic view [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2020: 9829-9838. [6] Zhang J, Li W, Wang P, et al. A large scale RGB-D dataset for action recognition [C]//International Workshop on Understanding Human Activities through 3D Sensors, 2016: 101-114. [7] Shi L, Zhang Y, Cheng J, et al. Skeleton-based action recognition with directed graph neural networks [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 7912-7921. [8] Yan S, Xiong Y, Lin D. Spatial temporal graph convolutional networks for skeleton-based action recognition [C]//Thirty-second AAAI Conference on Artificial Intelligence, 2018: 1-9. [9] 管珊珊, 张益农. 基于残差时空图卷积网络的3D人体行为识别[J]. 计算机应用与软件, 2020, 37(3): 198-201, 250. Guan S S, Zhang Y N. 3D human action recognition based on residual spatiotemporal graph convolutional networks [J]. Computer Applications and Software, 2020, 37(3): 198-201, 250. (in Chinese) [10] 李炫烨, 郝兴伟, 贾金公, 等. 结合多注意力机制与时空图卷积网络的人体动作识别方法[J]. 计算机辅助设计与图形学学报, 2021, 33(7): 1055-1063. Li X Y, Hao X W, Jia J G, et al. Human action recognition method combining multi-attention mechanism and spatio-temporal graph convolutional network [J]. Journal of Computer-Aided Design and Graphics, 2021, 33(7): 1055-1063. (in Chinese) [11] 李扬志, 袁家政, 刘宏哲. 基于时空注意力图卷积网络模型的人体骨架动作识别算法[J]. 计算机应用, 2021, 41(7): 1915-1921. Li Y Z, Yuan J Z, Liu H Z. Human skeleton action recognition algorithm based on spatiotemporal attention graph convolutional network model [J]. Computer Applications, 2021, 41(7): 1915-1921. (in Chinese) [12] Li M, Chen S, Zhao Y, et al. Dynamic multiscale graph neural networks for 3D skeleton based human motion prediction [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2020: 214-223. [13] Xiao Y P, Lai Y K, Zhang F L, et al. A survey on deep geometry learning: from a representation perspective [J]. Computational Visual Media, 2020, 6(2): 113-133. [14] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition [C]//IEEE International Conference on Intelligent Robots and Systems, 2015: 922- 928. [15] Su H, Maji S, Kalogerakis E, et al. Multi-view convolutional neural networks for 3D shape recognition [C]//IEEE International Conference on Computer Vision, 2015: 945-953. [16] Hanocka R, Hertz A, Fish N, et al. MeshCNN: a network with an edge [J]. ACM Transactions on Graphics, 2019, 38(4): 1-12. [17] Qi C R, Su H, Mo K, et al. PointNet: deep learning on point sets for 3D classification and segmentation [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 652- 660. [18] Charles R, Li Y, Hao S, et al. Deep hierarchical feature learning on point sets in a metric space [C]//Advances in Neural Information Processing Systems, 2017: 4-9. [19] Liu X, Yan M, Bohg J. MeteorNet: deep learning on dynamic 3D point cloud sequences [C]//IEEE International Conference on Computer Vision, 2019: 9246-9255. [20] Wang Y, Xiao Y, Xiong F, et al. 3DV: 3D dynamic voxel for action recognition in depth video [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2020: 511-520. [21] Veinidis C, Pratikakis I, Theoharis T. Unsupervised human action retrieval using salient points in 3D mesh sequences [J]. Multimedia Tools and Applications, 2019, 78(3): 2789-2814. [22] Zhang Y, Black M J, Tang S. We are more than our joints: predicting how 3D bodies move [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2021: 3372-3382. [23] Qiao Y L, Lai Y K, Fu H, et al. Synthesizing mesh deformation sequences with bidirectional LSTM [J]. IEEE Transactions on Visualization and Computer Graphics, 2022, 28(4): 1906-1916. [24] Bogo F, Romero J, Pons-Moll G, et al. Dynamic FAUST: registering human bodies in motion [C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6233-6242. [25] Mahmood N, Ghorbani N, Troje N F, et al. AMASS: archive of motion capture as surface shapes [C]//IEEE International Conference on Computer Vision, 2019: 5442-5451. |