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We present TIPSy-GAN, a new approach to improve the accuracy and stability inunsupervised adversarial 2D to 3D human pose estimation. In our work wedemonstrate that the human kinematic skeleton should not be assumed as a singlespatially codependent structure; in fact, we posit when a full 2D pose isprovided during training, there is an inherent bias learned where the 3Dcoordinate of a keypoint is spatially codependent on the 2D coordinates of allother keypoints. To investigate our hypothesis we follow previous adversarialapproaches but train two generators on spatially independent parts of thekinematic skeleton, the torso and the legs. We find that improving theself-consistency cycle is key to lowering the evaluation error and thereforeintroduce new consistency constraints during training. A TIPSy model isproduced via knowledge distillation from these generators which can predict the3D ordinates for the entire 2D pose with improved results. Furthermore, weaddress an unanswered question in prior work of how long to train in a trulyunsupervised scenario. We show that for two independent generators trainingadversarially has improved stability than that of a solo generator whichcollapses. TIPSy decreases the average error by 17\% when compared to that of abaseline solo generator on the Human3.6M dataset. TIPSy improves upon otherunsupervised approaches while also performing strongly against supervised andweakly-supervised approaches during evaluation on both the Human3.6M andMPI-INF-3DHP datasets.

"Teaching Independent Parts Separately" (TIPSy-GAN) : Improving Accuracy and Stability in Unsupervised Adversarial 2D to 3D Pose Estimation