Synthetic visual data can provide practically infinite diversity and rich
labels, while avoiding ethical issues with privacy and bias. However, for many
tasks, current models trained on synthetic data generalize poorly to real data.
The task of 3D human pose estimation is a particularly interesting example of
this sim2real problem, because learning-based approaches perform reasonably
well given real training data, yet labeled 3D poses are extremely difficult to
obtain in the wild, limiting scalability. In this paper, we show that standard
neural-network approaches, which perform poorly when trained on synthetic RGB
images, can perform well when the data is pre-processed to extract cues about
the person's motion, notably as optical flow and the motion of 2D keypoints.
Therefore, our results suggest that motion can be a simple way to bridge a
sim2real gap when video is available. We evaluate on the 3D Poses in the Wild
dataset, the most challenging modern benchmark for 3D pose estimation, where we
show full 3D mesh recovery that is on par with state-of-the-art methods trained
on real 3D sequences, despite training only on synthetic humans from the
SURREAL dataset.

通过提取运动信息（光流和二维关键点），我们展示了神经网络方法可以在仅使用 SURREAL 合成数据的情况下与最先进的基于真实 3D 序列训练的方法相媲美地从 3D 视角估计人体姿态。