  The availability of the large-scale labeled 3D poses in the Human3.6M dataset
plays an important role in advancing the algorithms for 3D human pose
estimation from a still image. We observe that recent innovation in this area
mainly focuses on new techniques that explicitly address the generalization
issue when using this dataset, because this database is constructed in a highly
controlled environment with limited human subjects and background variations.
Despite such efforts, we can show that the results of the current methods are
still error-prone especially when tested against the images taken in-the-wild.
In this paper, we aim to tackle this problem from a different perspective. We
propose a principled approach to generate high quality 3D pose ground truth
given any in-the-wild image with a person inside. We achieve this by first
devising a novel stereo inspired neural network to directly map any 2D pose to
high quality 3D counterpart. We then perform a carefully designed geometric
searching scheme to further refine the joints. Based on this scheme, we build a
large-scale dataset with 400,000 in-the-wild images and their corresponding 3D
pose ground truth. This enables the training of a high quality neural network
model, without specialized training scheme and auxiliary loss function, which
performs favorably against the state-of-the-art 3D pose estimation methods. We
also evaluate the generalization ability of our model both quantitatively and
qualitatively. Results show that our approach convincingly outperforms the
previous methods. We make our dataset and code publicly available.
