Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3-dimensional positions. With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feed-forward network outperforms the best reported result by about 30% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (i.e., using images as input) yields results on par with the state of the art -- this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of state-of-the-art deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.

通过构建一个借助于当前技术能在控制误差较低前提下从2D开环位置中提取3D位置任务的相对简单的深度前向网络，我们发现了现代深度3D姿态估计系统的可视分析引起的错误是其主要问题。

3D人体姿态估计的简单而有效的基准线