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A Unified Framework for Domain Adaptive Pose Estimation

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13693))

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Abstract

While pose estimation is an important computer vision task, it requires expensive annotation and suffers from domain shift. In this paper, we investigate the problem of domain adaptive 2D pose estimation that transfers knowledge learned on a synthetic source domain to a target domain without supervision. While several domain adaptive pose estimation models have been proposed recently, they are not generic but only focus on either human pose or animal pose estimation, and thus their effectiveness is somewhat limited to specific scenarios. In this work, we propose a unified framework that generalizes well on various domain adaptive pose estimation problems. We propose to align representations using both input-level and output-level cues (pixels and pose labels, respectively), which facilitates the knowledge transfer from the source domain to the unlabeled target domain. Our experiments show that our method achieves state-of-the-art performance under various domain shifts. Our method outperforms existing baselines on human pose estimation by up to 4.5 percent points (pp), hand pose estimation by up to 7.4 pp, and animal pose estimation by up to 4.8 pp for dogs and 3.3 pp for sheep. These results suggest that our method is able to mitigate domain shift on diverse tasks and even unseen domains and objects (e.g., trained on horse and tested on dog). Our code will be publicly available at: https://github.com/VisionLearningGroup/UDA_PoseEstimation.

D. Kim and K. Wang—Equal Contribution.

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References

  1. Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 39(12), 2481–2495 (2017)

    Article  Google Scholar 

  2. Damodaran, B.B., Kellenberger, B., Flamary, R., Tuia, D., Courty, N.: DeepJDOT: deep joint distribution optimal transport for unsupervised domain adaptation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 467–483. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01225-0_28

    Chapter  Google Scholar 

  3. Cao, J., Tang, H., Fang, H., Shen, X., Tai, Y., Lu, C.: Cross-domain adaptation for animal pose estimation. In: IEEE International Conference on Computer Vision (ICCV), pp. 9497–9506 (2019)

    Google Scholar 

  4. Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40, 834–848 (2018)

    Article  Google Scholar 

  5. Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223 (2016)

    Google Scholar 

  6. Deng, J., Dong, W., Socher, R., Li, L., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 248–255 (2009)

    Google Scholar 

  7. Devries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)

  8. French, G., Mackiewicz, M., Fisher, M.H.: Self-ensembling for visual domain adaptation. In: International Conference on Learning Representations (ICLR) (2018)

    Google Scholar 

  9. Ganin, Y., et al.: Domain-adversarial training of neural networks. J. Mach. Learn. Res. (JMLR) 17, 2030–2096 (2016)

    MathSciNet  Google Scholar 

  10. Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32, 1231–1237 (2013)

    Article  Google Scholar 

  11. Gretton, A., et al.: Optimal kernel choice for large-scale two-sample tests. In: Advances in Neural Information Processing Systems (NeurIPS) (2012)

    Google Scholar 

  12. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)

    Google Scholar 

  13. Hoffman, J., et al.: CyCADA: cycle-consistent adversarial domain adaptation. In: International Conference on Machine Learning (ICML), pp. 1994–2003 (2018)

    Google Scholar 

  14. Hoffman, J., Wang, D., Yu, F., Darrell, T.: FCNs in the wild: pixel-level adversarial and constraint-based adaptation. arXiv preprint arXiv:1612.02649 (2016)

  15. Huang, X., Belongie, S.J.: Arbitrary style transfer in real-time with adaptive instance normalization. In: IEEE International Conference on Computer Vision (ICCV), pp. 1510–1519 (2017)

    Google Scholar 

  16. 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. (TPAMI) 36, 1325–1339 (2014)

    Article  Google Scholar 

  17. Jiang, J., Ji, Y., Wang, X., Liu, Y., Wang, J., Long, M.: Regressive domain adaptation for unsupervised keypoint detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6780–6789 (2021)

    Google Scholar 

  18. Johnson, S., Everingham, M.: Clustered pose and nonlinear appearance models for human pose estimation. In: British Machine Vision Conference (BMVC), pp. 1–11 (2010)

    Google Scholar 

  19. Ke, L., Chang, M.-C., Qi, H., Lyu, S.: Multi-scale structure-aware network for human pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11206, pp. 731–746. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01216-8_44

    Chapter  Google Scholar 

  20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  21. Kundu, J.N., Lakkakula, N., Radhakrishnan, V.B.: UM-Adapt: unsupervised multi-task adaptation using adversarial cross-task distillation. In: IEEE International Conference on Computer Vision (ICCV), pp. 1436–1445 (2019)

    Google Scholar 

  22. Kundu, J.N., Uppala, P.K., Pahuja, A., Babu, R.V.: AdaDepth: unsupervised content congruent adaptation for depth estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2656–2665 (2018)

    Google Scholar 

  23. Li, C., Lee, G.H.: From synthetic to real: unsupervised domain adaptation for animal pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1482–1491 (2021)

    Google Scholar 

  24. 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, Cham (2015). https://doi.org/10.1007/978-3-319-16808-1_23

    Chapter  Google Scholar 

  25. Li, Y., Yuan, L., Vasconcelos, N.: Bidirectional learning for domain adaptation of semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6936–6945 (2019)

    Google Scholar 

  26. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, 7–12 June 2015, pp. 3431–3440 (2015)

    Google Scholar 

  27. Long, M., Cao, Y., Wang, J., Jordan, M.I.: Learning transferable features with deep adaptation networks. In: International Conference on Machine Learning (ICML), pp. 97–105 (2015)

    Google Scholar 

  28. Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems (NeurIPS), pp. 1640–1650 (2018)

    Google Scholar 

  29. Mu, J., Qiu, W., Hager, G.D., Yuille, A.L.: Learning from synthetic animals. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12383–12392 (2020)

    Google Scholar 

  30. 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_29

    Chapter  Google Scholar 

  31. Pero, L.D., Ricco, S., Sukthankar, R., Ferrari, V.: Articulated motion discovery using pairs of trajectories. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2151–2160 (2015)

    Google Scholar 

  32. Rodriguez, A.L., Mikolajczyk, K.: DESC: domain adaptation for depth estimation via semantic consistency. In: British Machine Vision Conference 2020 (BMVC) (2020)

    Google Scholar 

  33. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  34. Saito, K., Watanabe, K., Ushiku, Y., Harada, T.: Maximum classifier discrepancy for unsupervised domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3723–3732 (2018)

    Google Scholar 

  35. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) (2015)

    Google Scholar 

  36. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5693–5703 (2019)

    Google Scholar 

  37. Tarvainen, A., Valpola, H.: Mean teachers are better role models: weight-averaged consistency targets improve semi-supervised deep learning results. In: International Conference on Learning Representations (ICLR) (2017)

    Google Scholar 

  38. Tompson, J.J., Jain, A., LeCun, Y., Bregler, C.: Joint training of a convolutional network and a graphical model for human pose estimation. In: Advances in Neural Information Processing Systems (NeurIPS) (2014)

    Google Scholar 

  39. Tsai, Y., Hung, W., Schulter, S., Sohn, K., Yang, M., Chandraker, M.: Learning to adapt structured output space for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7472–7481 (2018)

    Google Scholar 

  40. Tzeng, E., Hoffman, J., Saenko, K., Darrell, T.: Adversarial discriminative domain adaptation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2962–2971 (2017)

    Google Scholar 

  41. Varol, G., et al.: Learning from synthetic humans. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4627–4635 (2017)

    Google Scholar 

  42. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11210, pp. 472–487. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01231-1_29

    Chapter  Google Scholar 

  43. Xie, R., Wang, C., Zeng, W., Wang, Y.: An empirical study of the collapsing problem in semi-supervised 2D human pose estimation. In: IEEE International Conference on Computer Vision (ICCV), pp. 11240–11249 (2021)

    Google Scholar 

  44. Yang, Y., Soatto, S.: FDA: Fourier domain adaptation for semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4084–4094 (2020)

    Google Scholar 

  45. Zhang, Y., Liu, T., Long, M., Jordan, M.: Bridging theory and algorithm for domain adaptation. In: International Conference on Machine Learning (ICML), pp. 7404–7413. PMLR (2019)

    Google Scholar 

  46. Zhao, Z., Wang, T., Xia, S., Wang, Y.: Hand-3D-Studio: a new multi-view system for 3D hand reconstruction. In: IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP, pp. 2478–2482 (2020)

    Google Scholar 

  47. Zimmermann, C., Brox, T.: Learning to estimate 3D hand pose from single RGB images. In: IEEE International Conference on Computer Vision (ICCV), pp. 4913–4921 (2017)

    Google Scholar 

  48. Zimmermann, C., Ceylan, D., Yang, J., Russell, B.C., Argus, M.J., Brox, T.: FreiHAND: a dataset for markerless capture of hand pose and shape from single RGB images. In: IEEE International Conference on Computer Vision (ICCV), pp. 813–822 (2019)

    Google Scholar 

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Acknowledgements

This work has been partially supported by NSF Award, DARPA, DARPA LwLL, ONR MURI grant N00014-19-1-2571 associated with AUSMURIB000001 (to M.B.) and by NSF grant 1535797, 1551572, (to. M.B.).

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Authors and Affiliations

  1. Image and Video Computing, Department of Computer Science, Boston University, Boston, USA

    Kaihong Wang, Kate Saenko, Margrit Betke & Stan Sclaroff

  2. MIT-IBM Watson AI Lab, Cambridge, USA

    Donghyun Kim & Kate Saenko

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  1. Donghyun Kim
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Correspondence to Donghyun Kim .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Kim, D., Wang, K., Saenko, K., Betke, M., Sclaroff, S. (2022). A Unified Framework for Domain Adaptive Pose Estimation. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13693. Springer, Cham. https://doi.org/10.1007/978-3-031-19827-4_35

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