The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
History Repeats Itself: Human Motion Prediction via Motion Attention
Open accessAbstract
Human motion prediction aims to forecast future human poses given a past motion. Whether based on recurrent or feed-forward neural networks, existing methods fail to model the observation that human motion tends to repeat itself, even for complex sports actions and cooking activities. Here, we introduce an attention-based feed-forward network that explicitly leverages this observation. In particular, instead of modeling frame-wise attention via pose similarity, we propose to extract motion attention to capture the similarity between the current motion context and the historical motion sub-sequences. Aggregating the relevant past motions and processing the result with a graph convolutional network allows us to effectively exploit motion patterns from the long-term history to predict the future poses. Our experiments on Human3.6M, AMASS and 3DPW evidence the benefits of our approach for both periodical and non-periodical actions. Thanks to our attention model, it yields state-of-the-art results on all three datasets. Our code is available at this https URL.
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Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
(2010) · International Conference on Machine Learning · Cited by 13,995
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · cited 4×
“functions we use to train our model on either 3D coordinates or joint angles. For 3D joint coordinates prediction, following [20], we make use of the Mean Per Joint Position Error (MPJPE) proposed in [10]. In particular, for one training sample, this yields the loss ‘= 1 J(M+ T) MX+T t=1 XJ j=1 kp^t;j pt;j k 2 ; (6) w…”
MethodInfluentialAMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00554 · Cited by 1,323
Kiros, R., Zhu, Y., Salakhutdinov, R., Zemel, R. S., Torralba, A., Urtasun, R., & Fidler, S. (2015) · arXiv · doi:10.48550/arxiv.1506.06726 · Cited by 1,293
Generating Text with Recurrent Neural Networks
Sutskever, I., Martens, J., & Hinton, G. E. (2011) · Cited by 1,264
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
Recovering Accurate 3D Human Pose in the Wild Using IMUs and a Moving Camera
von Marcard, T., Henschel, R., Black, M. J., Rosenhahn, B., & Pons-Moll, G. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01249-6_37 · Cited by 1,058
Romero (2017) · doi:10.1145/3130800.3130883 · Cited by 1,056
Cited by
Generating Human Motion from Textual Descriptions with Discrete Representations
Zhang, J., Zhang, Y., Cun, X., Zhang, Y., Zhao, H., Lu, H., Shen, X., & Ying, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01415 · Cited by 328
“Approaches mainly focus on efficiently and effectively fusing spatial and temporal information to generate deterministic future motion through different models: RNN [12,20,50,51], GAN [9,30], GCN [49], Attention [48] or even simply MLP [11, 17, 25].”
MethodSpatio-Temporal Branching for Motion Prediction using Motion Increments
Wang, J., Zhou, Y., Qiang, W., Ba, Y., Su, B., & Wen, J.-R. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3612330 · Cited by 11
Dynamic Compositional Graph Convolutional Network for Efficient Composite Human Motion Prediction
Zhang, W., Zhao, S., Meng, F., Wu, S., & Liu, M. (2023) · Proceedings of the 31st ACM International Conference on Multimedia · doi:10.1145/3581783.3612532 · Cited by 10
Self-citation
Metadata
- Authors
- Mao, W., Liu, M., & Salzmann, M.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2020
- Pages
- 474-489
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W3109717189
- Cited by
- 299
- References
- 26
- FWCI
- 63.24
- Type
- conference paper
- Language
- en
- Open access
- green
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