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Harvesting Multiple Views for Marker-Less 3D Human Pose Annotations
2017
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Recent advances with Convolutional Networks (ConvNets) have shifted the bottleneck for many computer vision tasks to annotated data collection. In this paper, we present a geometry-driven approach to automatically collect annotations for human pose prediction tasks. Starting from a generic ConvNet for 2D human pose, and assuming a multi-view setup, we describe an automatic way to collect accurate 3D human pose annotations. We capitalize on constraints offered by the 3D geometry of the camera
doi:10.1109/cvpr.2017.138
dblp:conf/cvpr/PavlakosZDD17
fatcat:gatlbmqhdzah5hfzejcqwpqxxq