Estimating human pose, shape, and motion from images and videos are
fundamental challenges with many applications. Recent advances in 2D human pose
estimation use large amounts of manually-labeled training data for learning
convolutional neural networks (CNNs). Such data is time consuming to acquire
and difficult to extend. Moreover, manual labeling of 3D pose, depth and motion
is impractical. In this work we present SURREAL (Synthetic hUmans foR REAL
tasks): a new large-scale dataset with synthetically-generated but realistic
images of people rendered from 3D sequences of human motion capture data. We
generate more than 6 million frames together with ground truth pose, depth
maps, and segmentation masks. We show that CNNs trained on our synthetic
dataset allow for accurate human depth estimation and human part segmentation
in real RGB images. Our results and the new dataset open up new possibilities
for advancing person analysis using cheap and large-scale synthetic data.

本文提出了使用由 3D 运动捕捉数据生成的合成真实人形图像的大规模数据集 (SURREAL) 来训练卷积神经网络 (CNNs)，并且通过该数据集训练的 CNNs 在 RGB 图像中可以准确地进行人物深度估计和人物部分分割。