A Survey of Recent Advances on Two-Step 3D Human Pose Estimation
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Springer International Publishing
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https://link.springer.com/content/pdf/10.1007/978-3-031-21689-3_20
Reference58 articles.
1. Banik, S., García, A.M., Knoll, A.: 3D human pose regression using graph convolutional network. In: 2021 IEEE International Conference on Image Processing (ICIP) pp. 924–928 (2021). https://doi.org/10.1109/ICIP42928.2021.9506736
2. Bartol, K., Bojanic, D., Petkovic, T., D’Apuzzo, N., Pribanic, T.: A review of 3D human pose estimation from 2D images. In: Proceedings of 3DBODY.TECH 2020–11th International Conference and Exhibition on 3D Body Scanning and Processing Technologies, Online/Virtual, 17–18 November 2020. Hometrica Consulting - Dr. Nicola D’Apuzzo (2020). https://doi.org/10.15221/20.29
3. Cao, Z., Hidalgo, G., Simon, T., Wei, S.E., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43(1), 172–186 (2019)
4. Chen, C., Ramanan, D.: 3D human pose estimation = 2D pose estimation + matching. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5759–5767. IEEE Computer Society, Los Alamitos, CA, USA (2017). https://doi.org/10.1109/CVPR.2017.610
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1. DUA: A Domain-Unified Approach for Cross-Dataset 3D Human Pose Estimation;Sensors;2023-08-22
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