[33] Ren, Y., Yang, J.: 3d human pose estimation for free-from and moving activities using wifi. arXiv preprint arXiv:2204.07878 (2022)
[34] Ren, Y., Zhao, C., He, Y., Cong, P., Liang, H., Yu, J., Xu, L., Ma, Y.: Lidar-aid inertial poser: Large-scale human motion capture by sparse inertial and lidar sensors. IEEE Transactions on Visualization and Computer Graphics 29(5), 2337–2347 (2023)
[35] Sanakoyeu, A., Khalidov, V., McCarthy, M.S., Vedaldi, A., Neverova, N.: Transferring dense pose to proximal animal classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)
[36] Sengupta, A., Jin, F., Zhang, R., Cao, S.: mm-posed: Real-time human skeletal posture estimation using mmwave radars and cnns. IEEE Sensors Journal 20(17), 10032-10044 (2020)
[37] Shahroudy, A., Liu, J., Ng, T.T., Wang, G.: Ntu rgb+ d: A large scale dataset for 3d human activity analysis. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1010-1019 (2016)
[38] Shi, L., Zhang, Y., Cheng, J., Lu, H.: Two-stream adaptive graph convolutional networks for skeleton-based action recognition. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12026-12035 (2019)
[39] Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: CVPR (2019)
[40] Von Marcard, T., Henschel, R., Black, M.J., Rosenhahn, B., Pons-Moll, G.: Recovering accurate 3d human pose in the wild using imus and a moving camera. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 601-617 (2018)
[41] Von Marcard, T., Pons-Moll, G., Rosenhahn, B.: Human pose estimation from video and imus. IEEE transactions on pattern analysis and machine intelligence 38(8), 1533-1547 (2016)
[42] Wang, Z., Gao, Z., Wang, L., Li, Z., Wu, G.: Boundary-aware cascade networks for temporal action segmentation. In: Computer Vision-ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16. pp. 34–51. Springer (2020)
[43] Xie, Y., Li, Z., Li, M.: Precise power delay profiling with commodity wifi. In: Proceedings of the 21st Annual international conference on Mobile Computing and Networking. pp. 53-64 (2015)
[44] Xue, H., Ju, Y., Miao, C., Wang, Y., Wang, S., Zhang, A., Su, L.: mmmesh: Towards 3d real-time dynamic human mesh construction using millimeter-wave. In: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services. pp. 269-282 (2021)
[45] Yan, M., Wang, X., Dai, Y., Shen, S., Wen, C., Xu, L., Ma, Y., Wang, C.: Cimi4d: A large multimodal climbing motion dataset under human-scene interactions. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12977-12988 (2023)
[46] Yang, J., Chen, X., Zou, H., Lu, C.X., Wang, D., Sun, S., Xie, L.: Sensefi: A library and benchmark on deep-learning-empowered wifi human sensing. Patterns 4(3) (2023)
[47] Yang, J., Zhou, Y., Huang, H., Zou, H., Xie, L.: Meta: Device-free pose estimation via commodity wifi for metaverse avatar simulation. In: IEEE World Forum on Internet of Things 2022 (2022)
[48] Yang, J., Zou, H., Jiang, H., Xie, L.: Device-free occupant activity sensing using wifi-enabled IoT devices for smart homes. IEEE Internet of Things Journal 5(5), 3991-4002 (2018)
[49] Yang, J., Zou, H., Zhou, Y., Xie, L.: Learning gestures from wifi: A siamese recurrent convolutional architecture. IEEE Internet of Things Journal 6(6), 10763-10772 (2019)
[50] Zhang, Z., Wang, C., Qin, W., Zeng, W.: Fusing wearable imus with multi-view images for human pose estimation: A geometric approach. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2200-2209 (2020)
[51] Zhao, H., Jiang, L., Jia, J., Torr, P.H., Koltun, V.: Point transformer. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 16259-16268 (2021)
[52] Zhao, M., Li, T., Abu Alsheikh, M., Tian, Y., Zhao, H., Torralba, A., Katabi, D.: Throughwall human pose estimation using radio signals. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7356-7365 (2018)