The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction
Abstract
We propose a novel attention-based framework for 3D human pose estimation from a monocular video. Despite the general success of end-to-end deep learning paradigms, our approach is based on two key observations: (1) temporal incoherence and jitter are often yielded from a single frame prediction; (2) error rate can be remarkably reduced by increasing the receptive field in a video. Therefore, we design an attentional mechanism to adaptively identify significant frames and tensor outputs from each deep neural net layer, leading to a more optimal estimation. To achieve large temporal receptive fields, multi-scale dilated convolutions are employed to model long-range dependencies among frames. The architecture is straightforward to implement and can be flexibly adopted for real-time applications. Any off-the-shelf 2D pose estimation system, e.g. Mocap libraries, can be easily integrated in an ad-hoc fashion. We both quantitatively and qualitatively evaluate our method on various standard benchmark datasets (e.g. Human3.6M, HumanEva). Our method considerably outperforms all the state-of-the-art algorithms up to 8% error reduction (average mean per joint position error: 34.7) as compared to the best-reported results. Code is available at: (https://github.com/lrxjason/Attention3DHumanPose)
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Cites
Deep Residual Learning for Image Recognition
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He, K., Gkioxari, G., Dollar, P., & Girshick, R. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.322 · Cited by 29,137
Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
Stacked Hourglass Networks for Human Pose Estimation
Newell, A., Yang, K., & Deng, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_29 · Cited by 5,634
WaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
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Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
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Cited by
MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video
Zhang, J., Tu, Z., Yang, J., Chen, Y., & Yuan, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01288 · Cited by 426
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
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
- Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S.-C., & Asari, V.
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 5063-5072
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3034448411
- Cited by
- 266
- References
- 49
- FWCI
- 10.22
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liu, R., Shen, J., Wang, H., Chen, C., Cheung, S.-C., & Asari, V. (2020). Attention Mechanism Exploits Temporal Contexts: Real-Time 3D Human Pose Reconstruction. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr42600.2020.00511
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