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Recent advances with Convolutional Networks (ConvNets) have shifted thebottleneck for many computer vision tasks to annotated data collection. In thispaper, we present a geometry-driven approach to automatically collectannotations for human pose prediction tasks. Starting from a generic ConvNetfor 2D human pose, and assuming a multi-view setup, we describe an automaticway to collect accurate 3D human pose annotations. We capitalize on constraintsoffered by the 3D geometry of the camera setup and the 3D structure of thehuman body to probabilistically combine per view 2D ConvNet predictions into aglobally optimal 3D pose. This 3D pose is used as the basis for harvestingannotations. The benefit of the annotations produced automatically with ourapproach is demonstrated in two challenging settings: (i) fine-tuning a genericConvNet-based 2D pose predictor to capture the discriminative aspects of asubject's appearance (i.e.,"personalization"), and (ii) training a ConvNet fromscratch for single view 3D human pose prediction without leveraging 3D posegroundtruth. The proposed multi-view pose estimator achieves state-of-the-artresults on standard benchmarks, demonstrating the effectiveness of our methodin exploiting the available multi-view information.

Harvesting Multiple Views for Marker-less 3D Human Pose Annotations