  Human 3D pose estimation from a single image is a challenging task with
numerous applications. Convolutional Neural Networks (CNNs) have recently
achieved superior performance on the task of 2D pose estimation from a single
image, by training on images with 2D annotations collected by crowd sourcing.
This suggests that similar success could be achieved for direct estimation of
3D poses. However, 3D poses are much harder to annotate, and the lack of
suitable annotated training images hinders attempts towards end-to-end
solutions. To address this issue, we opt to automatically synthesize training
images with ground truth pose annotations. Our work is a systematic study along
this road. We find that pose space coverage and texture diversity are the key
ingredients for the effectiveness of synthetic training data. We present a
fully automatic, scalable approach that samples the human pose space for
guiding the synthesis procedure and extracts clothing textures from real
images. Furthermore, we explore domain adaptation for bridging the gap between
our synthetic training images and real testing photos. We demonstrate that CNNs
trained with our synthetic images out-perform those trained with real photos on
3D pose estimation tasks.
