  This paper addresses the problem of 3D human pose estimation in the wild. A
significant challenge is the lack of training data, i.e., 2D images of humans
annotated with 3D poses. Such data is necessary to train state-of-the-art CNN
architectures. Here, we propose a solution to generate a large set of
photorealistic synthetic images of humans with 3D pose annotations. We
introduce an image-based synthesis engine that artificially augments a dataset
of real images with 2D human pose annotations using 3D motion capture data.
Given a candidate 3D pose, our algorithm selects for each joint an image whose
2D pose locally matches the projected 3D pose. The selected images are then
combined to generate a new synthetic image by stitching local image patches in
a kinematically constrained manner. The resulting images are used to train an
end-to-end CNN for full-body 3D pose estimation. We cluster the training data
into a large number of pose classes and tackle pose estimation as a $K$-way
classification problem. Such an approach is viable only with large training
sets such as ours. Our method outperforms most of the published works in terms
of 3D pose estimation in controlled environments (Human3.6M) and shows
promising results for real-world images (LSP). This demonstrates that CNNs
trained on artificial images generalize well to real images. Compared to data
generated from more classical rendering engines, our synthetic images do not
require any domain adaptation or fine-tuning stage.
