We present TIPSy-GAN, a new approach to improve the accuracy and stability in unsupervised adversarial 2D to 3D human pose estimation. In our work we demonstrate that the human kinematic skeleton should not be assumed as a single spatially codependent structure; in fact, we posit when a full 2D pose is provided during training, there is an inherent bias learned where the 3D coordinate of a keypoint is spatially codependent on the 2D coordinates of all other keypoints. To investigate our hypothesis we follow previous adversarial approaches but train two generators on spatially independent parts of the kinematic skeleton, the torso and the legs. We find that improving the self-consistency cycle is key to lowering the evaluation error and therefore introduce new consistency constraints during training. A TIPSy model is produced via knowledge distillation from these generators which can predict the 3D ordinates for the entire 2D pose with improved results. Furthermore, we address an unanswered question in prior work of how long to train in a truly unsupervised scenario. We show that for two independent generators training adversarially has improved stability than that of a solo generator which collapses. TIPSy decreases the average error by 17\% when compared to that of a baseline solo generator on the Human3.6M dataset. TIPSy improves upon other unsupervised approaches while also performing strongly against supervised and weakly-supervised approaches during evaluation on both the Human3.6M and MPI-INF-3DHP datasets.

翻译：我们提出了TIPSy-GAN,这是提高未经监督的对立对立2D至3D人构成估计的准确性和稳定性的新办法。我们在工作中表明,不应将人体运动骨骼假定为单一的空间共独立结构;事实上,我们假设在培训期间提供完整的 2D 构成时,一个关键点的3D协调在空间上取决于所有其他关键点的2D坐标,就会产生固有的偏差。为了调查我们的假设,我们遵循了先前的对立方法,但在运动骨骼的空间独立部分、托尔索和腿上培训了两台发电机。我们发现,改进自我一致性周期对于降低评价错误至关重要,因此在培训期间引入新的一致性限制。一个TIPSy模型是通过这些发电机的知识蒸馏产生的,这些发电机可以预测整个2D的关键点的3D坐标与改进的结果。此外,我们在先前的工作中未解答一个问题,即:在真正不受监督的情况下,如何在运动骨架上对两台独立的人体运动进行空间独立培训,TERS-3腿。我们发现,在测试期间,对弱的人类发动机进行培训,对18度数据进行对比期间,对18度数据进行第17次的分析也大大下降。