Abstract
This paper deals with the estimation of hand pose from a single depth image. We present a method that is based on a description of the hand pose via local rotations of bones trained discriminatively in an end-to-end fashion using a convolutional neural network. We compare our method with existing approach of hand pose estimation of 3D locations of hand joints. For this purpose, we collected precise ground-truth data with a passive marker-based optical motion capture technology. The results show, that the estimation of the hand pose formulated as a combination of local rotations of bones and relative locations of joints outperforms the direct estimation of 3D global joints locations.
This work was supported by the European Regional Development Fund under the project AI&Reasoning (reg. no. CZ.02.1.01/0.0/0.0/15 003/0000466). This work was supported by the Ministry of Education of the Czech Republic, project No. LTARF18017. Access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
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Notes
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The sign language speaker who interprets the spoken language to be used for signing avatar broadcasting.
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References
Choi, C., Kim, S., Ramani, K.: Learning hand articulations by hallucinating heat distribution. In: 2017 IEEE International Conference on Computer Vision (ICCV), pp. 3123–3132, October 2017. https://doi.org/10.1109/ICCV.2017.337
Feix, T., Romero, J., Ek, C.H., Schmiedmayer, H.B., Kragic, D.: A metric for comparing the anthropomorphic motion capability of artificial hands. IEEE Trans. Rob. 29(1), 82–93 (2013). https://doi.org/10.1109/TRO.2012.2217675
Ge, Y., Liang, H., Yuan, J., Thalmann, D.: Robust 3D hand pose estimation in single depth images: from single-view CNN to multi-view CNNS. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2016), June 2016
Oberweger, M., Lepetit, V.: Deepprior++: Improving fast and accurate 3D hand pose estimation. In: 2017 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2017, Venice, Italy, 22–29 October 2017, pp. 585–594 (2017). https://doi.org/10.1109/ICCVW.2017.75
Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. In: CVWW, February 2015
Simon, T., Joo, H., Matthews, I., Sheikh, Y.: Hand keypoint detection in single images using multiview bootstrapping. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Sinha, A., Choi, C., Ramani, K.: Deephand: robust hand pose estimation by completing a matrix imputed with deep features. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016
Sun, X., Wei, Y., Liang, S., Tang, X., Sun, J.: Cascaded hand pose regression. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015
Supancic, J.S., Rogez, G., Yang, Y., Shotton, J., Ramanan, D.: Depth-based hand pose estimation: data, methods, and challenges. In: The IEEE International Conference on Computer Vision (ICCV), December 2015
Tang, D., Jin Chang, H., Tejani, A., Kim, T.K.: Latent regression forest: structured estimation of 3D articulated hand posture. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014
Tang, D., Yu, T.H., Kim, T.K.: Real-time articulated hand pose estimation using semi-supervised transductive regression forests. In: Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 3224–3231. ICCV 2013. IEEE Computer Society, Washington (2013). https://doi.org/10.1109/ICCV.2013.400
Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph. 33(5), 169:1–169:10 (2014). https://doi.org/10.1145/2629500
Wan, C., Probst, T., Van Gool, L., Yao, A.: Crossing nets: combining GANs and VAEs with a shared latent space for hand pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017
Wetzler, A., Slossberg, R., Kimmel, R.: Rule of thumb: deep derotation for improved fingertip detection. In: BMVC (2015)
Yuan, S., Ye, Q., Stenger, B., Jain, S., Kim, T.K.: BigHand2. 2M benchmark: hand pose data set and state of the art analysis. In: CVPR, July 2017
Zhao, W., Chai, J., Xu, Y.Q.: Combining marker-based mocap and RGB-D camera for acquiring high-fidelity hand motion data. In: Lee, J., Kry, P. (eds.) Eurographics/ACM SIGGRAPH Symposium on Computer Animation. The Eurographics Association (2012). https://doi.org/10.2312/SCA/SCA12/033-042
Zhou, X., Wan, Q., Zhang, W., Xue, X., Wei, Y.: Model-based deep hand pose estimation. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 2421–2427. AAAI Press (2016). http://dl.acm.org/citation.cfm?id=3060832.3060960
Zimmermann, C., Brox, T.: Learning to estimate 3D hand pose from single RGB images. In: The IEEE International Conference on Computer Vision (ICCV), October 2017
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Kanis, J., Krňoul, Z., Hrúz, M. (2019). Combination of Positions and Angles for Hand Pose Estimation. In: Salah, A., Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2019. Lecture Notes in Computer Science(), vol 11658. Springer, Cham. https://doi.org/10.1007/978-3-030-26061-3_22
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