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The availability of the large-scale labeled 3D poses in the Human3.6M datasetplays an important role in advancing the algorithms for 3D human poseestimation from a still image. We observe that recent innovation in this areamainly focuses on new techniques that explicitly address the generalizationissue when using this dataset, because this database is constructed in a highlycontrolled environment with limited human subjects and background variations.Despite such efforts, we can show that the results of the current methods arestill 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. Wepropose a principled approach to generate high quality 3D pose ground truthgiven any in-the-wild image with a person inside. We achieve this by firstdevising a novel stereo inspired neural network to directly map any 2D pose tohigh quality 3D counterpart. We then perform a carefully designed geometricsearching scheme to further refine the joints. Based on this scheme, we build alarge-scale dataset with 400,000 in-the-wild images and their corresponding 3Dpose ground truth. This enables the training of a high quality neural networkmodel, without specialized training scheme and auxiliary loss function, whichperforms favorably against the state-of-the-art 3D pose estimation methods. Wealso evaluate the generalization ability of our model both quantitatively andqualitatively. Results show that our approach convincingly outperforms theprevious methods. We make our dataset and code publicly available.

Generalizing Monocular 3D Human Pose Estimation in the Wild