Advertisement

A Semi-supervised Deep Generative Model for Human Body Analysis

  • Rodrigo de BemEmail author
  • Arnab Ghosh
  • Thalaiyasingam Ajanthan
  • Ondrej Miksik
  • N. Siddharth
  • Philip Torr
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11130)

Abstract

Deep generative modelling for human body analysis is an emerging problem with many interesting applications. However, the latent space learned by such models is typically not interpretable, resulting in less flexible models. In this work, we adopt a structured semi-supervised approach and present a deep generative model for human body analysis where the body pose and the visual appearance are disentangled in the latent space. Such a disentanglement allows independent manipulation of pose and appearance, and hence enables applications such as pose-transfer without being explicitly trained for such a task. In addition, our setting allows for semi-supervised pose estimation, relaxing the need for labelled data. We demonstrate the capabilities of our generative model on the Human3.6M and on the DeepFashion datasets.

Keywords

Deep generative models Variational autoencoders Semi-supervised learning Human pose estimation Analysis-by-synthesis 

Notes

Acknowledgements

Rodrigo Andrade de Bem is a CAPES Foundation scholarship holder (Process no: 99999.013296/2013-02, Ministry of Education, Brazil). Ondrej Miksik is currently with Emotech Labs. This work was supported by the EPSRC, ERC grant ERC-2012-AdG 321162-HELIOS, EPSRC grant Seebibyte EP/M013774/1 and EPSRC/MURI grant EP/N019474/1.

References

  1. 1.
    Achilles, F., Ichim, A.-E., Coskun, H., Tombari, F., Noachtar, S., Navab, N.: Patient MoCap: human pose estimation under blanket occlusion for hospital monitoring applications. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9900, pp. 491–499. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46720-7_57CrossRefGoogle Scholar
  2. 2.
    de Bem, R., Arnab, A., Sapienza, M., Golodetz, S., Torr, P.: Deep fully-connected part-based models for human pose estimation. In: ACML (2018)Google Scholar
  3. 3.
    de Bem, R., Ghosh, A., Ajanthan, T., Siddharth, N., Torr, P.: A conditional deep generative model of people in natural images. In: WACV (2019)Google Scholar
  4. 4.
    Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9911, pp. 717–732. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46478-7_44CrossRefGoogle Scholar
  5. 5.
    Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2D pose estimation using part affinity fields. In: CVPR (2017)Google Scholar
  6. 6.
    Chu, X., Yang, W., Ouyang, W., Ma, C., Yuille, A.L., Wang, X.: Multi-context attention for human pose estimation. In: CVPR (2017)Google Scholar
  7. 7.
    Elgammal, A., Lee, C.S.: Inferring 3D body pose from silhouettes using activity manifold learning. In: CVPR (2004)Google Scholar
  8. 8.
    Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning. MIT press, Cambridge (2016)zbMATHGoogle Scholar
  9. 9.
    Goodfellow, I., et al.: Generative adversarial nets. In: NIPS (2014)Google Scholar
  10. 10.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. In: ICCV (2015)Google Scholar
  11. 11.
    Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3.6M: large scale datasets and predictive methods for 3D human sensing in natural environments. TPAMI 36, 1325–1339 (2014)CrossRefGoogle Scholar
  12. 12.
    Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: CVPR (2017)Google Scholar
  13. 13.
    Jaeggli, T., Koller-Meier, E., Van Gool, L.: Learning generative models for monocular body pose estimation. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007. LNCS, vol. 4843, pp. 608–617. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-76386-4_57CrossRefGoogle Scholar
  14. 14.
    Jia, Y., et al.: Caffe: convolutional architecture for fast feature embedding. In: ACMMM (2014)Google Scholar
  15. 15.
    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: ICLR (2018)Google Scholar
  16. 16.
    Kingma, D., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (2015)Google Scholar
  17. 17.
    Kingma, D.P., Mohamed, S., Rezende, D.J., Welling, M.: Semi-supervised learning with deep generative models. In: NIPS (2014)Google Scholar
  18. 18.
    Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: ICLR (2014)Google Scholar
  19. 19.
    Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.: Deep convolutional inverse graphics network. In: NIPS (2015)Google Scholar
  20. 20.
    Larsen, A.B.L., Sønderby, S.K., Larochelle, H., Winther, O.: Autoencoding beyond pixels using a learned similarity metric. In: ICML (2016)Google Scholar
  21. 21.
    Lassner, C., Pons-Moll, G., Gehler, P.V.: A generative model for people in clothing. In: ICCV (2017)Google Scholar
  22. 22.
    Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: CVPR (2016)Google Scholar
  23. 23.
    Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Gool, L.V.: Pose guided person image generation. In: NIPS (2017)Google Scholar
  24. 24.
    Ma, L., Sun, Q., Georgoulis, S., Van Gool, L., Schiele, B., Fritz, M.: Disentangled person image generation. In: CVPR (2018)Google Scholar
  25. 25.
    Massiceti, D., Siddharth, N., Dokania, P., Torr, P.H.: FlipDial: a generative model for two-way visual dialogue. In: CVPR (2018)Google Scholar
  26. 26.
    Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 483–499. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46484-8_29CrossRefGoogle Scholar
  27. 27.
    Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: ICML (2014)Google Scholar
  28. 28.
    Schulman, J., Heess, N., Weber, T., Abbeel, P.: Gradient estimation using stochastic computation graphs. In: NIPS (2015)Google Scholar
  29. 29.
    Seemann, E., Nickel, K., Stiefelhagen, R.: Head pose estimation using stereo vision for human-robot interaction. In: FG (2004)Google Scholar
  30. 30.
    Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: CVPR (2011)Google Scholar
  31. 31.
    Siarohin, A., Sangineto, E., Lathuiliere, S., Sebe, N.: Deformable GANs for pose-based human image generation. In: CVPR (2018)Google Scholar
  32. 32.
    Siddharth, N., et al.: Learning disentangled representations with semi-supervised deep generative models. In: NIPS (2017)Google Scholar
  33. 33.
    Sigal, L., Balan, A., Black, M.J.: Combined discriminative and generative articulated pose and non-rigid shape estimation. In: NIPS (2008)Google Scholar
  34. 34.
    Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2Face: real-time face capture and reenactment of RGB videos. In: CVPR (2016)Google Scholar
  35. 35.
    Tompson, J., Jain, A., LeCun, Y., Bregler, C.: Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation. In: NIPS (2014)Google Scholar
  36. 36.
    Tulyakov, S., Liu, M., Yang, X., Kautz, J.: MoCoGAN: decomposing motion and content for video generation. In: CVPR (2018)Google Scholar
  37. 37.
    von Marcard, T., Rosenhahn, B., Black, M., Pons-Moll, G.: Sparse inertial poser: automatic 3D human pose estimation from sparse IMUs. Eurographics (2017)Google Scholar
  38. 38.
    Walker, J., Marino, K., Gupta, A., Hebert, M.: The pose knows: video forecasting by generating pose futures. In: ICCV (2017)Google Scholar
  39. 39.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. TIP 13, 600–612 (2004)Google Scholar
  40. 40.
    Wang, Z., Merel, J.S., Reed, S.E., de Freitas, N., Wayne, G., Heess, N.: Robust imitation of diverse behaviors. In: NIPS (2017)Google Scholar
  41. 41.
    Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: CVPR (2016)Google Scholar
  42. 42.
    Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: ICCV (2017)Google Scholar
  43. 43.
    Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: CVPR (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Rodrigo de Bem
    • 1
    • 2
    Email author
  • Arnab Ghosh
    • 1
  • Thalaiyasingam Ajanthan
    • 1
  • Ondrej Miksik
    • 1
  • N. Siddharth
    • 1
  • Philip Torr
    • 1
  1. 1.Department of Engineering ScienceUniversity of OxfordOxfordUK
  2. 2.Center of Computational SciencesFederal University of Rio GrandeRio GrandeBrazil

Personalised recommendations