3D human pose estimation is a challenging task because of the difficulty to acquire ground-truth data outside of controlled environments. A number of further issues have been hindering progress in building a universal and robust model for this task, including domain gaps between different datasets, unseen actions between train and test datasets, various hardware settings and high cost of annotation, etc. In this paper, we propose an algorithm to generate infinite 3D synthetic human poses (Legatus) from a 3D pose distribution based on 10 initial handcrafted 3D poses (Decanus) during the training of a 2D to 3D human pose lifter neural network. Our results show that we can achieve 3D pose estimation performance comparable to methods using real data from specialized datasets but in a zero-shot setup, showing the generalization potential of our framework.

翻译：由于难以在受控环境之外获得地面真实数据,3D人造外表估计是一项艰巨的任务。 其他一些问题一直阻碍着在为这项任务建立一个普遍和稳健的模式方面取得进展,包括不同数据集之间的领域差距、火车和测试数据集之间的无形行动、各种硬件设置和注释成本高等等。 在本文件中,我们提出了一个算法,从3D人造外表(Legatus)中产生无限的3D合成人造外表(Legatus),根据培训2D至3D人造外表神经网络时10种最初手工制作的3D型外表(Decanus)进行分配。 我们的结果表明,我们可以实现3D的估算表现与使用专门数据集的实际数据的方法相当,但采用零弹式组合,显示了我们框架的概括潜力。