The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video
Open accessAbstract
Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation. We observe that the motions of different joints differ significantly. However, the previous methods cannot efficiently model the solid inter-frame correspondence of each joint, leading to insufficient learning of spatial-temporal correlation. We propose MixSTE (Mixed Spatio-Temporal Encoder), which has a temporal transformer block to separately model the temporal motion of each joint and a spatial transformer block to learn inter-joint spatial correlation. These two blocks are utilized alternately to obtain better spatio-temporal feature encoding. In addition, the network output is extended from the central frame to entire frames of the input video, thereby improving the coherence between the input and output sequences. Extensive experiments are conducted on three benchmarks (i.e. Human3.6M, MPI-INF-3DHP, and HumanEva). The results show that our model outperforms the state-of-the-art approach by 10.9% P-MPJPE and 7.6% MPJPE. The code is available at https://github.com/JinluZhang1126/MixSTE.
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Deep High-Resolution Representation Learning for Human Pose Estimation
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MotionBERT: A Unified Perspective on Learning Human Motion Representations
Zhu, W., Ma, X., Liu, Z., Liu, L., Wu, W., & Wang, Y. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01385 · Cited by 348
SkeletonMAE: Spatial-Temporal Masked Autoencoders for Self-Supervised Skeleton Action Recognition
Wu, W., Hua, Y., Zheng, C., Wu, S., Chen, C., & Lu, A. (2023) · 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) · doi:10.1109/icmew59549.2023.00045 · Cited by 60
A Systematic Review of Recent Deep Learning Approaches for 3D Human Pose Estimation
El Kaid, A., & Baïna, K. (2023) · Journal of Imaging · doi:10.3390/jimaging9120275 · Cited by 44
Metadata
- Authors
- Zhang, J., Tu, Z., Yang, J., Chen, Y., & Yuan, J.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 13222-13232
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312417903
- Cited by
- 426
- References
- 53
- FWCI
- 31.87
- Type
- conference paper
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video. https://app.cochat.ai/papers/10.1109/cvpr52688.2022.01288
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- Zhang, J., Tu, Z., Yang, J., Chen, Y., & Yuan, J. (2022). MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.01288
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