Abstract
Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art.
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References
Abavisani, M., Joze, H.R.V., Patel, V.M.: Improving the performance of unimodal dynamic hand-gesture recognition with multimodal training. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019
Boukhayma, A., Bem, R.D., Torr, P.H.: 3D hand shape and pose from images in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10843–10852 (2019)
Cai, Y., Ge, L., Cai, J., Magnenat-Thalmann, N., Yuan, J.: 3D hand pose estimation using synthetic data and weakly labeled RGB images. IEEE Trans. Pattern Anal. Mach. Intell. PP, 1 (2020)
Cai, Y., Ge, L., Cai, J., Yuan, J.: Weakly-supervised 3D hand pose estimation from monocular RGB images. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 666–682 (2018)
Cai, Y., et al.: Exploiting spatial-temporal relationships for 3D pose estimation via graph convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2272–2281 (2019)
Cai, Y., Huang, L., et al.: Learning progressive joint propagation for human motion prediction. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
Carreira, J., Zisserman, A.: Quo vadis, action recognition? A new model and the kinetics dataset. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6299–6308 (2017)
Chen, X., Lin, K.Y., Liu, W., Qian, C., Lin, L.: Weakly-supervised discovery of geometry-aware representation for 3D human pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 10895–10904 (2019)
De Smedt, Q., Wannous, H., Vandeborre, J.P.: Skeleton-based dynamic hand gesture recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2016)
Garcia-Hernando, G., Yuan, S., Baek, S., Kim, T.K.: First-person hand action benchmark with RGB-D videos and 3D hand pose annotations. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Hu, J.F., Zheng, W.S., Lai, J., Zhang, J.: Jointly learning heterogeneous features for RGB-D activity recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Iqbal, U., Garbade, M., Gall, J.: Pose for action-action for pose. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 438–445. IEEE (2017)
Klaser, A., Marszałek, M., Schmid, C.: A spatio-temporal descriptor based on 3D-gradients (2008)
Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005). https://doi.org/10.1007/s11263-005-1838-7
Liu, J., Shahroudy, A., Xu, D., Kot, A.C., Wang, G.: Skeleton-based action recognition using spatio-temporal LSTM network with trust gates. IEEE Trans. Pattern Anal. Mach. Intell. 40(12), 3007–3021 (2018)
Liu, J., Wang, G., Duan, L., Abdiyeva, K., Kot, A.C.: Skeleton-based human action recognition with global context-aware attention LSTM networks. IEEE Trans. Image Process. 27(4), 1586–1599 (2018)
Liu, J., et al.: Feature boosting network for 3D pose estimation. IEEE Trans. Pattern Anal. Mach. Intell. 42, 494–501 (2019)
Liu, J., Shahroudy, A., Xu, D., Wang, G.: Spatio-temporal LSTM with trust gates for 3D human action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 816–833. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_50
Liu, J., Wang, G., Hu, P., Duan, L.Y., Kot, A.C.: Global context-aware attention LSTM networks for 3d action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1647–1656 (2017)
Liu, M., Yuan, J.: Recognizing human actions as the evolution of pose estimation maps. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Luvizon, D.C., Picard, D., Tabia, H.: 2D/3D pose estimation and action recognition using multitask deep learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018
Mueller, F., et al.: Ganerated hands for real-time 3D hand tracking from monocular RGB. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 49–59 (2018)
Nguyen, X.S., Brun, L., Lezoray, O., Bougleux, S.: A neural network based on SPD manifold learning for skeleton-based hand gesture recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019
Oreifej, O., Liu, Z.: HON4D: histogram of oriented 4D normals for activity recognition from depth sequences. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013
Rad, M., Oberweger, M., Lepetit, V.: Domain transfer for 3D pose estimation from color images without manual annotations. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11365, pp. 69–84. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20873-8_5
Rahmani, H., Mian, A.: 3D action recognition from novel viewpoints. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Simonyan, K., Zisserman, A.: Two-stream convolutional networks for action recognition in videos. In: Advances in Neural Information Processing Systems, pp. 568–576 (2014)
Tekin, B., Bogo, F., Pollefeys, M.: H+O: unified egocentric recognition of 3D hand-object poses and interactions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6450–6459 (2018)
Tu, Z., et al.: Multi-stream CNN: learning representations based on human-related regions for action recognition. Pattern Recogn. 79, 32–43 (2018)
Wang, H., Kläser, A., Schmid, C., Liu, C.L.: Dense trajectories and motion boundary descriptors for action recognition. Int. J. Comput. Vis. 103(1), 60–79 (2013). https://doi.org/10.1007/s11263-012-0594-8
Wang, H., Schmid, C.: Action recognition with improved trajectories. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3551–3558 (2013)
Wang, L., et al.: Temporal segment networks: towards good practices for deep action recognition. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 20–36. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_2
Xiaohan Nie, B., Xiong, C., Zhu, S.C.: Joint action recognition and pose estimation from video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1293–1301 (2015)
Xie, S., Sun, C., Huang, J., Tu, Z., Murphy, K.: Rethinking spatiotemporal feature learning: speed-accuracy trade-offs in video classification. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 305–321 (2018)
Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: Towards 3D human pose estimation in the wild: a weakly-supervised approach. In: The IEEE International Conference on Computer Vision (ICCV), October 2017
Zhu, H., Vial, R., Lu, S.: TORNADO: a spatio-temporal convolutional regression network for video action proposal. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5813–5821 (2017)
Zhu, H., et al.: YoTube: searching action proposal via recurrent and static regression networks. IEEE Trans. Image Process. 27(6), 2609–2622 (2018)
Zimmermann, C., Brox, T.: Learning to estimate 3D hand pose from single RGB images. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4903–4911 (2017)
Acknowledgement
The research was carried out at the Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Singapore. This research work was partially supported by SUTD projects PIE-SGP-Al-2020-02 and SRG-ISTD-2020-153.
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Yang, S., Liu, J., Lu, S., Er, M.H., Kot, A.C. (2020). Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham. https://doi.org/10.1007/978-3-030-58580-8_45
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