Built on

Gower, J.C.: Generalized procrustes analysis. Psychometrika 40
1975
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 248–255. IEEE (2009)
2009
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Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: New benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. pp. 3686–3693 (2014)
2014
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Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36
2014
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Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
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. pp. 1799–1807 (2014)
2014
Earlier work this paper cites.
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., Zhang, Z.: Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)
Original
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Original
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 648–656 (2015)
2015
Earlier work this paper cites.
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it smpl: Automatic estimation of 3d human pose and shape from a single image. In: European Conference on Computer Vision. pp. 561–578. Springer (2016)
2016
Earlier work this paper cites.
Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: European Conference on Computer Vision. pp. 717–732. Springer (2016)
2016
Earlier work this paper cites.
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1611.08050 (2016)
Original
2016
Earlier work this paper cites.
Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4733–4742 (2016)
2016
Earlier work this paper cites.
Chen, C.H., Ramanan, D.: 3d human pose estimation= 2d pose estimation+ matching. arXiv preprint arXiv:1612.06524 (2016)
Original
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357 (2016)
Original
2016
Earlier work this paper cites.
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)
2016
Earlier work this paper cites.
Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In: European Conference on Computer Vision. pp. 34–50. Springer (2016)
2016
Earlier work this paper cites.

Similar

Levine, S., Finn, C., Darrell, T., Abbeel, P.: End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research 17
2016
Cited alongside, same era.
Mehta, D., Rhodin, H., Casas, D., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3d human pose estimation in the wild using improved cnn supervision. arXiv preprint arXiv:1611.09813 (2016)
Original
2016
Cited alongside, same era.
Moreno-Noguer, F.: 3d human pose estimation from a single image via distance matrix regression. arXiv preprint arXiv:1611.09010 (2016)
Original
2016
Cited alongside, same era.
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision. pp. 483–499. Springer (2016)
2016
Cited alongside, same era.
Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3d human pose. arXiv preprint arXiv:1611.07828 (2016)

Then

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. arXiv preprint arXiv:1703.06211 (2017)
Original
2017
Closest in time.
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask r-cnn. In: International Conference on Computer Vision (2017)
2017
Closest in time.
Hossain, M.R.I., Little, J.J.: Exploiting temporal information for 3d pose estimation. arXiv preprint arXiv:1711.08585 (2017)
Original
2017
Closest in time.
Jahangiri, E., Yuille, A.L.: Generating multiple hypotheses for human 3d pose consistent with 2d joint detections. arXiv preprint arXiv:1702.02258 (2017)
Original
2017
Closest in time.
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. arXiv preprint arXiv:1712.06584 (2017)

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2017

Integral Human Pose Regression

Sun, Xiao, Xiao, Bin, Wei, Fangyin et al.

Understand

State-of-the-art human pose estimation methods are based on heat map representation.

In spite of the good performance, the representation has a few issues in nature, such as not differentiable and quantization error.
This work shows that a simple integral operation relates and unifies the heat map representation and joint regression, thus avoiding the above issues.
It is differentiable, efficient, and compatible with any heat map based methods.

Built on

Gower, J.C.: Generalized procrustes analysis. Psychometrika 40
1975
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 248–255. IEEE (2009)
2009
Earlier work this paper cites.
Andriluka, M., Pishchulin, L., Gehler, P., Schiele, B.: 2d human pose estimation: New benchmark and state of the art analysis. In: Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. pp. 3686–3693 (2014)
2014
Earlier work this paper cites.
Ionescu, C., Papava, D., Olaru, V., Sminchisescu, C.: Human3. 6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence 36
2014
Earlier work this paper cites.
Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: European conference on computer vision. pp. 740–755. Springer (2014)
2014
Earlier work this paper cites.
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. pp. 1799–1807 (2014)
2014
Earlier work this paper cites.
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., Zhang, Z.: Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274 (2015)
Original
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)
Original
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems. pp. 91–99 (2015)
2015
Earlier work this paper cites.
Tompson, J., Goroshin, R., Jain, A., LeCun, Y., Bregler, C.: Efficient object localization using convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 648–656 (2015)
2015
Earlier work this paper cites.
Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., Black, M.J.: Keep it smpl: Automatic estimation of 3d human pose and shape from a single image. In: European Conference on Computer Vision. pp. 561–578. Springer (2016)
2016
Earlier work this paper cites.
Bulat, A., Tzimiropoulos, G.: Human pose estimation via convolutional part heatmap regression. In: European Conference on Computer Vision. pp. 717–732. Springer (2016)
2016
Earlier work this paper cites.
Cao, Z., Simon, T., Wei, S.E., Sheikh, Y.: Realtime multi-person 2d pose estimation using part affinity fields. arXiv preprint arXiv:1611.08050 (2016)
Original
2016
Earlier work this paper cites.
Carreira, J., Agrawal, P., Fragkiadaki, K., Malik, J.: Human pose estimation with iterative error feedback. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4733–4742 (2016)
2016
Earlier work this paper cites.
Chen, C.H., Ramanan, D.: 3d human pose estimation= 2d pose estimation+ matching. arXiv preprint arXiv:1612.06524 (2016)
Original
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. arXiv preprint arXiv:1610.02357 (2016)
Original
2016
Earlier work this paper cites.
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)
2016
Earlier work this paper cites.
Insafutdinov, E., Pishchulin, L., Andres, B., Andriluka, M., Schiele, B.: Deepercut: A deeper, stronger, and faster multi-person pose estimation model. In: European Conference on Computer Vision. pp. 34–50. Springer (2016)
2016
Earlier work this paper cites.

Similar

Levine, S., Finn, C., Darrell, T., Abbeel, P.: End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research 17
2016
Cited alongside, same era.
Mehta, D., Rhodin, H., Casas, D., Sotnychenko, O., Xu, W., Theobalt, C.: Monocular 3d human pose estimation in the wild using improved cnn supervision. arXiv preprint arXiv:1611.09813 (2016)
Original
2016
Cited alongside, same era.
Moreno-Noguer, F.: 3d human pose estimation from a single image via distance matrix regression. arXiv preprint arXiv:1611.09010 (2016)
Original
2016
Cited alongside, same era.
Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: European Conference on Computer Vision. pp. 483–499. Springer (2016)
2016
Cited alongside, same era.
Pavlakos, G., Zhou, X., Derpanis, K.G., Daniilidis, K.: Coarse-to-fine volumetric prediction for single-image 3d human pose. arXiv preprint arXiv:1611.07828 (2016)

Then

Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., Wei, Y.: Deformable convolutional networks. arXiv preprint arXiv:1703.06211 (2017)
Original
2017
Closest in time.
He, K., Gkioxari, G., Dollar, P., Girshick, R.: Mask r-cnn. In: International Conference on Computer Vision (2017)
2017
Closest in time.
Hossain, M.R.I., Little, J.J.: Exploiting temporal information for 3d pose estimation. arXiv preprint arXiv:1711.08585 (2017)
Original
2017
Closest in time.
Jahangiri, E., Yuille, A.L.: Generating multiple hypotheses for human 3d pose consistent with 2d joint detections. arXiv preprint arXiv:1702.02258 (2017)
Original
2017
Closest in time.
Kanazawa, A., Black, M.J., Jacobs, D.W., Malik, J.: End-to-end recovery of human shape and pose. arXiv preprint arXiv:1712.06584 (2017)

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Original

2016

Cited alongside, same era.

Pishchulin, L., Insafutdinov, E., Tang, S., Andres, B., Andriluka, M., Gehler, P.V., Schiele, B.: Deepcut: Joint subset partition and labeling for multi person pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4929–4937 (2016)

2016

Cited alongside, same era.

Rafi, U., Kostrikov, I., Gall, J., Leibe, B.: An efficient convolutional network for human pose estimation. In: BMVC. vol. 1, p. 2 (2016)

2016

Cited alongside, same era.

Rogez, G., Schmid, C.: Mocap-guided data augmentation for 3d pose estimation in the wild. In: Advances in Neural Information Processing Systems. pp. 3108–3116 (2016)

2016

Cited alongside, same era.

Tekin, B., Rozantsev, A., Lepetit, V., Fua, P.: Direct prediction of 3d body poses from motion compensated sequences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 991–1000 (2016)

2016

Cited alongside, same era.

Wei, S.E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4724–4732 (2016)

2016

Cited alongside, same era.

Yasin, H., Iqbal, U., Kruger, B., Weber, A., Gall, J.: A dual-source approach for 3d pose estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4948–4956 (2016)

2016

Cited alongside, same era.

Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: Lift: Learned invariant feature transform. In: European Conference on Computer Vision. pp. 467–483. Springer (2016)

2016

Cited alongside, same era.

Original

2017

Closest in time.

Luvizon, D.C., Tabia, H., Picard, D.: Human pose regression by combining indirect part detection and contextual information. arXiv preprint arXiv:1710.02322 (2017)

Original

2017

Closest in time.

Martinez, J., Hossain, R., Romero, J., Little, J.J.: A simple yet effective baseline for 3d human pose estimation. arXiv preprint arXiv:1705.03098 (2017)

Original

2017

Closest in time.

Nie, B.X., Wei, P., Zhu, S.C.: Monocular 3d human pose estimation by predicting depth on joints. In: Proc. IEEE Int. Conf. Comput. Vis. pp. 3447–3455 (2017)

2017

Closest in time.

Papandreou, G., Zhu, T., Kanazawa, N., Toshev, A., Tompson, J., Bregler, C., Murphy, K.: Towards accurate multi-person pose estimation in the wild. arXiv preprint arXiv:1701.01779 (2017)

Original

2017

Closest in time.

Sun, X., Shang, J., Liang, S., Wei, Y.: Compositional human pose regression. In: International Conference on Computer Vision (2017)

2017

Closest in time.

Tekin, B., Marquez Neila, P., Salzmann, M., Fua, P.: Learning to fuse 2d and 3d image cues for monocular body pose estimation. In: International Conference on Computer Vision (ICCV). No. EPFL-CONF-230311 (2017)

2017

Closest in time.

Thewlis, J., Bilen, H., Vedaldi, A.: Unsupervised learning of object landmarks by factorized spatial embeddings. In: Proc. ICCV (2017)

2017

Closest in time.

Tome, D., Russell, C., Agapito, L.: Lifting from the deep: Convolutional 3d pose estimation from a single image. arXiv preprint arXiv:1701.00295 (2017)

Original

2017

Closest in time.

Yang, W., Li, S., Ouyang, W., Li, H., Wang, X.: Learning feature pyramids for human pose estimation. In: The IEEE International Conference on Computer Vision (ICCV). vol. 2 (2017)

2017

Closest in time.

Zhou, X., Zhu, M., Pavlakos, G., Leonardos, S., Derpanis, K.G., Daniilidis, K.: Monocap: Monocular human motion capture using a cnn coupled with a geometric prior. arXiv preprint arXiv:1701.02354 (2017)

Original

2017

Closest in time.

Zhou, X., Huang, Q., Sun, X., Xue, X., Wei, Y.: Towards 3d human pose estimation in the wild: a weakly-supervised approach. In: International Conference on Computer Vision (2017)

2017

Closest in time.

Fang, H.S., Xu, Y., Wang, W., Liu, X., Zhu, S.C.: Learning pose grammar to encode human body configuration for 3d pose estimation. In: AAAI (2018)

2018

Closest in time.

Nibali, A., He, Z., Morgan, S., Prendergast, L.: Numerical coordinate regression with convolutional neural networks. arXiv preprint arXiv:1801.07372 (2018)

Original

2018

Closest in time.