In this work, we demonstrate that 3D poses in video can be effectively
estimated with a fully convolutional model based on dilated temporal
convolutions over 2D keypoints. We also introduce back-projection, a simple and
effective semi-supervised training method that leverages unlabeled video data.
We start with predicted 2D keypoints for unlabeled video, then estimate 3D
poses and finally back-project to the input 2D keypoints. In the supervised
setting, our fully-convolutional model outperforms the previous best result
from the literature by 6 mm mean per-joint position error on Human3.6M,
corresponding to an error reduction of 11%, and the model also shows
significant improvements on HumanEva-I. Moreover, experiments with
back-projection show that it comfortably outperforms previous state-of-the-art
results in semi-supervised settings where labeled data is scarce. Code and
models are available at this https URL

本文提出了一种基于卷积神经网络的 3D 姿势估计方法，并结合反向投影技术实现了半监督训练，结果表明该模型在无监督和半监督情况下准确性都得到了明显提高。