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Learning articulated object pose is inherently difficult because the pose is high dimensional but has many structural constraints.
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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)
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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)
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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)
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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)
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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)
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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)
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