Open Access
| Issue |
ITM Web Conf.
Volume 80, 2025
2025 2nd International Conference on Advanced Computer Applications and Artificial Intelligence (ACAAI 2025)
|
|
|---|---|---|
| Article Number | 01002 | |
| Number of page(s) | 9 | |
| Section | Machine Learning & Deep Learning Algorithms | |
| DOI | https://doi.org/10.1051/itmconf/20258001002 | |
| Published online | 16 December 2025 | |
- Q. Zhao, C. Zheng, M. Liu, P. Wang, C. Chen, Poseformerv2: Exploring frequency domain for efficient and robust 3D human pose estimation, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8877–8886 (2023) [Google Scholar]
- J. Wang, S. Tan, X. Zhen, S. Xu, F. Zheng, Z. He, L. Shao, Deep 3D human pose estimation: A review. Comput. Vis. Image Underst. 210, 103225 (2021) [Google Scholar]
- G. Pavlakos, X. Zhou, K.G. Derpanis, K. Daniilidis, Coarse-to-fine volumetric prediction for single-image 3D human pose, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7025–7034 (2017) [Google Scholar]
- S. Park, J. Hwang, N. Kwak, 3D human pose estimation using convolutional neural networks with 2D pose information, in Proceedings of the European Conference on Computer Vision, 156–169 (2016) [Google Scholar]
- C. Zheng, W. Wu, C. Chen, T. Yang, S. Zhu, J. Shen, M. Shah, Deep learning-based human pose estimation: A survey. ACM Comput. Surv. 56(1), 1–37 (2023) [Google Scholar]
- C. Zheng, S. Zhu, M. Mendieta, T. Yang, C. Chen, Z. Ding, 3D human pose estimation with spatial and temporal transformers, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 11656–11665 (2021) [Google Scholar]
- S. Tang, C. Li, P. Zhang, R.N. Tang, SwinLSTM: Improving spatiotemporal prediction accuracy using Swin Transformer and LSTM, in Proceedings of the IEEE/CVF International Conference on Computer Vision, 13470–13479 (2023) [Google Scholar]
- C. Ionescu, D. Papava, V. Olaru, C. Sminchisescu, Human3.6M: Large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2013) [Google Scholar]
- D. Pavllo, C. Feichtenhofer, D. Grangier, M. Auli, 3D human pose estimation in video with temporal convolutions and semi-supervised training, in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7753–7762 (2019) [Google Scholar]
- D. Mehta, H. Rhodin, D. Casas, P. Fua, O. Sotnychenko, W. Xu, C. Theobalt, Monocular 3D human pose estimation in the wild using improved CNN supervision, in Proceedings of the International Conference on 3D Vision (3DV), 506–516 (2017) [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.