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We propose a CNN-based approach for 3D human body pose estimation from singleRGB images that addresses the issue of limited generalizability of modelstrained solely on the starkly limited publicly available 3D pose data. Usingonly the existing 3D pose data and 2D pose data, we show state-of-the-artperformance on established benchmarks through transfer of learned features,while also generalizing to in-the-wild scenes. We further introduce a newtraining set for human body pose estimation from monocular images of realhumans that has the ground truth captured with a multi-camera marker-lessmotion capture system. It complements existing corpora with greater diversityin pose, human appearance, clothing, occlusion, and viewpoints, and enables anincreased scope of augmentation. We also contribute a new benchmark that coversoutdoor and indoor scenes, and demonstrate that our 3D pose dataset showsbetter in-the-wild performance than existing annotated data, which is furtherimproved in conjunction with transfer learning from 2D pose data. All in all,we argue that the use of transfer learning of representations in tandem withalgorithmic and data contributions is crucial for general 3D body poseestimation.

Monocular 3D Human Pose Estimation In The Wild Using Improved CNN Supervision