We present an unsupervised learning approach to recover 3D human pose from 2D skeletal joints extracted from a single image. Our method does not require any multi-view image data, 3D skeletons, correspondences between 2D-3D points, or use previously learned 3D priors during training. A lifting network accepts 2D landmarks as inputs and generates a corresponding 3D skeleton estimate. During training, the recovered 3D skeleton is reprojected on random camera viewpoints to generate new "synthetic" 2D poses. By lifting the synthetic 2D poses back to 3D and re-projecting them in the original camera view, we can define self-consistency loss both in 3D and in 2D. The training can thus be self supervised by exploiting the geometric self-consistency of the lift-reproject-lift process. We show that self-consistency alone is not sufficient to generate realistic skeletons, however adding a 2D pose discriminator enables the lifter to output valid 3D poses. Additionally, to learn from 2D poses "in the wild", we train an unsupervised 2D domain adapter network to allow for an expansion of 2D data. This improves results and demonstrates the usefulness of 2D pose data for unsupervised 3D lifting. Results on Human3.6M dataset for 3D human pose estimation demonstrate that our approach improves upon the previous unsupervised methods by 30% and outperforms many weakly supervised approaches that explicitly use 3D data.

通过采用无监督学习技术，基于来自单个图像的2D骨架关节，我们提出了一种恢复3D人体姿势的方法，该方法不需要任何多视图图像数据、3D骨架、2D-3D点之间的对应关系或在训练过程中使用先前学习的3D先验知识。我们的方法采用一个lifting网络将2D关键点作为输入，并生成相应的3D骨架估计，其中我们通过在随机相机视点重投影修复后的3D骨架来产生新的“合成”2D姿态。我们还训练了一个2D域适配器网络来扩展2D数据，并通过自适应正则化机制使域适应效果更好。最终，在Human3.6M数据集上，我们的方法对无监督三维lifting的改进达到了30％，并且优于许多明确使用3D数据的弱监督方法。

基于几何自监督的无监督三维姿态估计