Abstract
Human pose prediction, interchangeably known as human pose forecasting, is a daunting endeavor within computer vision. Owing to its pivotal role in many advanced applications and research avenues like smart surveillance, autonomous vehicles, and healthcare, human pose prediction models must exhibit high precision and efficacy to curb error dissemination, especially in real-world settings. In this paper, we unveil GAT-POSE, an innovative fusion framework marrying the strengths of graph autoencoders and transformers crafted for deterministic future pose prediction. Our methodology encapsulates a singular compression and tokenization of pose sequences through graph autoencoders. By harnessing a transformer architecture for pose prediction and capitalizing on the tokenized pose sequences, we construct a new paradigm for precise pose prediction. The robustness of GAT-POSE is ascertained through its deployment in three diverse training and testing ecosystems, coupled with the utilization of multiple datasets for a thorough appraisal. The stringency of our experimental setup underscores that GAT-POSE outperforms contemporary methodologies in human pose prediction, bearing significant promise to influence a variety of real-world applications favorably and lay a robust foundation for subsequent explorations in computer vision research.
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References
Ahmed, S., Huda, M.N., Rajbhandari, S., Saha, C., Elshaw, M., Kanarachos, S.: Pedestrian and cyclist detection and intent estimation for autonomous vehicles: a survey. Appl. Sci. 9(11), 2335 (2019)
Aliakbarian, S., Saleh, F.S., Salzmann, M., Petersson, L., Gould, S.: A stochastic conditioning scheme for diverse human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5223ā5232 (2020)
Barsoum, E., Kender, J., Liu, Z.: HP-GAN: probabilistic 3D human motion prediction via GAN. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1418ā1427 (2018)
BĆ¼tepage, J., Kjellstrƶm, H., Kragic, D.: Anticipating many futures: online human motion prediction and generation for human-robot interaction. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 4563ā4570. IEEE (2018)
Chao, X., et al.: Adversarial refinement network for human motion prediction. In: Proceedings of the Asian Conference on Computer Vision (2020)
Corona, E., Pumarola, A., Alenya, G., Moreno-Noguer, F.: Context-aware human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6992ā7001 (2020)
Cui, Q., Sun, H., Yang, F.: Learning dynamic relationships for 3D human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6519ā6527 (2020)
Cui, Q., Sun, H., Yang, F.: Learning dynamic relationships for 3D human motion prediction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6519ā6527 (2020)
Guo, X., Choi, J.: Human motion prediction via learning local structure representations and temporal dependencies. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.Ā 33, pp. 2580ā2587 (2019)
Huang, Z., Liu, Y., Fang, Y., Horn, B.K.: Video-based fall detection for seniors with human pose estimation. In: 2018 4th International Conference on Universal Village (UV), pp.Ā 1ā4. IEEE (2018)
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6m: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325ā1339 (2013)
Jain, D.K., Zareapoor, M., Jain, R., Kathuria, A., Bachhety, S.: GAN-poser: an improvised bidirectional GAN model for human motion prediction. Neural Comput. Appl. 32(18), 14579ā14591 (2020)
Jeon, H., Yoon, Y., Kim, D.: Lightweight 2D human pose estimation for fitness coaching system. In: 2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), pp.Ā 1ā4. IEEE (2021)
Kundu, J.N., Gor, M., Babu, R.V.: BiHMP-GAN: bidirectional 3D human motion prediction GAN. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.Ā 33, pp. 8553ā8560 (2019)
Li, C., Zhang, Z., Lee, W.S., Lee, G.H.: Convolutional sequence to sequence model for human dynamics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5226ā5234 (2018)
Li, C., Zhang, Z., Lee, W.S., Lee, G.H.: Convolutional sequence to sequence model for human dynamics. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5226ā5234 (2018)
Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: Dynamic multiscale graph neural networks for 3D skeleton based human motion prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 214ā223 (2020)
Li, M., Chen, S., Zhao, Y., Zhang, Y., Wang, Y., Tian, Q.: Multiscale spatio-temporal graph neural networks for 3D skeleton-based motion prediction. IEEE Trans. Image Process. 30, 7760ā7775 (2021)
Li, Y., et al.: Efficient convolutional hierarchical autoencoder for human motion prediction. Vis. Comput. 35, 1143ā1156 (2019)
Liu, D., Li, Q., Li, S., Kong, J., Qi, M.: Non-autoregressive sparse transformer networks for pedestrian trajectory prediction. Appl. Sci. 13(5), 3296 (2023)
Liu, S., Huang, X., Fu, N., Li, C., Su, Z., Ostadabbas, S.: Simultaneously-collected multimodal lying pose dataset: enabling in-bed human pose monitoring. IEEE Trans. Pattern Anal. Mach. Intell. 45(1), 1106ā1118 (2022)
Liu, X., Yin, J., Liu, J., Ding, P., Liu, J., Liu, H.: TrajectoryCNN: a new spatio-temporal feature learning network for human motion prediction. IEEE Trans. Circuits Syst. Video Technol. 31(6), 2133ā2146 (2020)
Liu, Z., et al.: Motion prediction using trajectory cues. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 13299ā13308 (2021)
Lyu, K., Chen, H., Liu, Z., Zhang, B., Wang, R.: 3D human motion prediction: a survey. Neurocomputing 489, 345ā365 (2022)
Lyu, K., Liu, Z., Wu, S., Chen, H., Zhang, X., Yin, Y.: Learning human motion prediction via stochastic differential equations. In: Proceedings of the 29th ACM International Conference on Multimedia, pp. 4976ā4984 (2021)
Ma, T., Nie, Y., Long, C., Zhang, Q., Li, G.: Progressively generating better initial guesses towards next stages for high-quality human motion prediction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6437ā6446 (2022)
Mahdavian, M., Nikdel, P., TaherAhmadi, M., Chen, M.: STPOTR: simultaneous human trajectory and pose prediction using a non-autoregressive transformer for robot follow-ahead. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9959ā9965. IEEE (2023)
Mandal, S., Biswas, S., Balas, V.E., Shaw, R.N., Ghosh, A.: Motion prediction for autonomous vehicles from Lyft dataset using deep learning. In: 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), pp. 768ā773. IEEE (2020)
Mangalam, K., Adeli, E., Lee, K.H., Gaidon, A., Niebles, J.C.: Disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 2784ā2793 (2020)
Mao, W., Liu, M., Salzmann, M.: History repeats itself: human motion prediction via motion attention. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12359, pp. 474ā489. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58568-6_28
Mao, W., Liu, M., Salzmann, M., Li, H.: Learning trajectory dependencies for human motion prediction. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2019)
Martinez, J., Black, M.J., Romero, J.: On human motion prediction using recurrent neural networks. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2891ā2900 (2017)
MartĆnez-GonzĆ”lez, A., Villamizar, M., Odobez, J.M.: Pose transformers (POTR): human motion prediction with non-autoregressive transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2276ā2284 (2021)
Medsker, L.R., Jain, L.: Recurrent neural networks. Des. Appl. 5(64ā67), 2 (2001)
Nikdel, P., Mahdavian, M., Chen, M.: DMMGAN: diverse multi motion prediction of 3D human joints using attention-based generative adversarial network. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 9938ā9944. IEEE (2023)
Noghre, G.A., Pazho, A.D., Katariya, V., Tabkhi, H.: Understanding the challenges and opportunities of pose-based anomaly detection. arXiv preprint arXiv:2303.05463 (2023)
Pazho, A.D., et al.: Ancilia: scalable intelligent video surveillance for the artificial intelligence of things. IEEE Internet Things J. (2023)
Saadatnejad, S., etĀ al.: A generic diffusion-based approach for 3D human pose prediction in the wild. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 8246ā8253 (2023). https://doi.org/10.1109/ICRA48891.2023.10160399
Saadatnejad, S., et al.: A generic diffusion-based approach for 3D human pose prediction in the wild. In: 2023 IEEE International Conference on Robotics and Automation (ICRA), pp. 8246ā8253. IEEE (2023)
Sofianos, T., Sampieri, A., Franco, L., Galasso, F.: Space-time-separable graph convolutional network for pose forecasting. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 11209ā11218 (2021)
Tang, Y., et al.: Flag3D: a 3D fitness activity dataset with language instruction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 22106ā22117 (2023)
Wang, H., Dong, J., Cheng, B., Feng, J.: PVRED: a position-velocity recurrent encoder-decoder for human motion prediction. IEEE Trans. Image Process. 30, 6096ā6106 (2021)
Wang, Y., Wang, X., Jiang, P., Wang, F.: RNN-based human motion prediction via differential sequence representation. In: 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS), pp. 138ā143. IEEE (2019)
Yang, X., Ren, X., Chen, M., Wang, L., Ding, Y.: Human posture recognition in intelligent healthcare. In: Journal of Physics: Conference Series, vol.Ā 1437, p. 012014. IOP Publishing (2020)
Yu, H., et al.: Towards realistic 3D human motion prediction with a spatio-temporal cross-transformer approach. IEEE Trans. Circuits Syst. Video Technol. (2023)
Yu, S., et al.: Regularity learning via explicit distribution modeling for skeletal video anomaly detection. IEEE Trans. Circuits Syst. Video Technol. (2023)
Yuan, Y., Kitani, K.: DLow: diversifying latent flows for diverse human motion prediction. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 346ā364. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_20
Zhong, C., Hu, L., Zhang, Z., Ye, Y., Xia, S.: Spatio-temporal gating-adjacency GCN for human motion prediction. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6447ā6456 (2022)
Zimmermann, C., Welschehold, T., Dornhege, C., Burgard, W., Brox, T.: 3D human pose estimation in RGBD images for robotic task learning. In: 2018 IEEE International Conference on Robotics and Automation (ICRA), pp. 1986ā1992. IEEE (2018)
Zou, J., et al.: Intelligent fitness trainer system based on human pose estimation. In: Sun, S., Fu, M., Xu, L. (eds.) ICSINC 2018. LNEE, vol. 550, pp. 593ā599. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-7123-3_69
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Pazho, A.D., Maldonado, G., Tabkhi, H. (2024). GAT-POSE: Graph Autoencoder-Transformer Fusion forĀ Future Pose Prediction. In: Filipe, J., Rƶning, J. (eds) Robotics, Computer Vision and Intelligent Systems. ROBOVIS 2024. Communications in Computer and Information Science, vol 2077. Springer, Cham. https://doi.org/10.1007/978-3-031-59057-3_11
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