nach oben

Erschienen in:

2016 | OriginalPaper | Buchkapitel

Deep Kinematic Pose Regression

verfasst von : Xingyi Zhou, Xiao Sun, Wei Zhang, Shuang Liang, Yichen Wei

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints. Most existing work do not model such constraints and does not guarantee the geometric validity of their pose estimation, therefore requiring a post-processing to recover the correct geometry if desired, which is cumbersome and sub-optimal. In this work, we propose to directly embed a kinematic object model into the deep neutral network learning for general articulated object pose estimation. The kinematic function is defined on the appropriately parameterized object motion variables. It is differentiable and can be used in the gradient descent based optimization in network training. The prior knowledge on the object geometric model is fully exploited and the structure is guaranteed to be valid. We show convincing experiment results on a toy example and the 3D human pose estimation problem. For the latter we achieve state-of-the-art result on Human3.6M dataset.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Overcoming Occlusion with Inverse Graphics

Nächstes Kapitel How Useful Is Photo-Realistic Rendering for Visual Learning?

Akhter, I., Black, M.J.: Pose-conditioned joint angle limits for 3D human pose reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1446–1455 (2015)

Belagiannis, V., Amin, S., Andriluka, M., Schiele, B., Navab, N., Ilic, S.: 3D pictorial structures for multiple human pose estimation. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014

Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: http://arxiv.org/abs/1607.08128

Cao, C., Weng, Y., Zhou, S., Tong, Y., Zhou, K.: Facewarehouse: a 3D facial expression database for visual computing. IEEE Trans. Vis. Comput. Graph. 20(3), 413–425 (2014)CrossRef

Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

Choi, C., Sinha, A., Hee Choi, J., Jang, S., Ramani, K.: A collaborative filtering approach to real-time hand pose estimation. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005)CrossRef

Ge, L., Liang, H., Yuan, J., Thalmann, D.: Robust 3D hand pose estimation in single depth images: from single-view CNN to multi-view CNNs. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

10.

Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: http://arxiv.org/abs/1605.03170

11.

Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)

12.

Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. IEEE Trans. Pattern Anal. Mach. Intell. 36(7), 1325–1339 (2014)CrossRef

13.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)

14.

Johnson, S., Everingham, M.: Learning effective human pose estimation from inaccurate annotation. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1465–1472. IEEE (2011)

15.

Jourabloo, A., Liu, X.: Large-pose face alignment via CNN-based dense 3D model fitting. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

16.

Li, S., Chan, A.B.: 3D human pose estimation from monocular images with deep convolutional neural network. In: Cremers, D., Reid, I., Saito, H., Yang, M.-H. (eds.) ACCV 2014. LNCS, vol. 9004, pp. 332–347. Springer, Heidelberg (2015). doi:10.1007/978-3-319-16808-1_23

17.

Li, S., Zhang, W., Chan, A.B.: Maximum-margin structured learning with deep networks for 3D human pose estimation. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

18.

Meyer, G.P., Gupta, S., Frosio, I., Reddy, D., Kautz, J.: Robust model-based 3D head pose estimation. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

19.

Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. CoRR abs/1603.06937 (2016). http://arxiv.org/abs/1603.06937

20.

Oberweger, M., Wohlhart, P., Lepetit, V.: Hands deep in deep learning for hand pose estimation. arXiv preprint arXiv:1502.06807 (2015)

21.

Oberweger, M., Wohlhart, P., Lepetit, V.: Training a feedback loop for hand pose estimation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3316–3324 (2015)

22.

Paysan, P., Knothe, R., Amberg, B., Romdhani, S., Vetter, T.: A 3D face model for pose and illumination invariant face recognition. In: Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, pp. 296–301. IEEE (2009)

23.

Pishchulin, L., Andriluka, M., Gehler, P., Schiele, B.: Poselet conditioned pictorial structures. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2013

24.

Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M.S., Berg, A.C., Li, F.: Imagenet large scale visual recognition challenge. CoRR abs/1409.0575 (2014). http://arxiv.org/abs/1409.0575

25.

Sharp, T., Keskin, C., Robertson, D., Taylor, J., Shotton, J., Kim, D., Rhemann, C., Leichter, I., Vinnikov, A., Wei, Y., Freedman, D., Kohli, P., Krupka, E., Fitzgibbon, A., Izadi, S.: Accurate, robust, and flexible realtime hand tracking. In: CHI (2015)

26.

Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M., Moore, R., Kohli, P., Criminisi, A., Kipman, A., et al.: Efficient human pose estimation from single depth images. IEEE Trans. Pattern Anal. Mach. Intell. 35(12), 2821–2840 (2013)CrossRef

27.

Sun, X., Wei, Y., Liang, S., Tang, X., Sun, J.: Cascaded hand pose regression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 824–832 (2015)

28.

Supancic III., J.S., Rogez, G., Yang, Y., Shotton, J., Ramanan, D.: Depth-based hand pose estimation: methods, data, and challenges. arXiv preprint arXiv:1504.06378 (2015)

29.

Tekin, B., Katircioglu, I., Salzmann, M., Lepetit, V., Fua, P.: Structured prediction of 3D human pose with deep neural networks. arXiv preprint arXiv:1605.05180 (2016)

30.

Tekin, B., Rozantsev, A., Lepetit, V., Fua, P.: Direct prediction of 3D body poses from motion compensated sequences. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

31.

Tompson, J., Stein, M., Lecun, Y., Perlin, K.: Real-time continuous pose recovery of human hands using convolutional networks. ACM Trans. Graph. 33, 169 (2014)CrossRef

32.

Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)

33.

Wang, C., Wang, Y., Lin, Z., Yuille, A.L., Gao, W.: Robust estimation of 3D human poses from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014

34.

Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

35.

Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1385–1392. IEEE (2011)

36.

Yasin, H., Iqbal, U., Kruger, B., Weber, A., Gall, J.: A dual-source approach for 3D pose estimation from a single image. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

37.

Yu, X., Zhou, F., Chandraker, M.: Deep deformation network for object landmark localization. arXiv preprint arXiv:1605.01014 (2016)

38.

Zhou, X., Leonardos, S., Hu, X., Daniilidis, K.: 3D shape estimation from 2D landmarks: a convex relaxation approach. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

39.

Zhou, X., Zhu, M., Leonardos, S., Derpanis, K.G., Daniilidis, K.: Sparseness meets deepness: 3D human pose estimation from monocular video. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

40.

Zhou, X., Wan, Q., Zhang, W., Xue, X., Wei, Y.: Model-based deep hand pose estimation. In: IJCAI (2016)

41.

Zhu, M., Zhou, X., Daniilidis, K.: Single image pop-up from discriminatively learned parts. In: The IEEE International Conference on Computer Vision (ICCV), December 2015

Titel: Deep Kinematic Pose Regression
verfasst von: Xingyi Zhou
Xiao Sun
Wei Zhang
Shuang Liang
Yichen Wei
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2016 Workshops
Print ISBN: 978-3-319-49408-1

Electronic ISBN: 978-3-319-49409-8

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-49409-8_17

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"