A Multiview Approach for Pedestrian 3D Pose Detection and Reconstruction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-7225-4_7
Reference29 articles.
1. Cao, Z., Hidalgo, M.G., Simon, T.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. IEEE Trans. Pattern Anal. Mach. Intell. 43, 172–186 (2021)
2. Zhang, W.Q., Fang, J., Wang, X.G.: EfficientPose: efficient human pose estimation with neural architecture search. Comput. Vis. Media 7, 335–347 (2021)
3. Sun, K., Xiao, B., Liu, D.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, NJ, pp. 5693–5703. IEEE (2019)
4. Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, NJ, pp. 1653–1660. IEEE (2014)
5. Sun, Y., Ye, Y., Liu, W.: Human mesh recovery from monocular images via a skeleton-disentangled representation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, NJ, pp. 5349–5358. IEEE (2019)
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