3D Pose Estimation using Synthetic Data over Monocular Depth Images
نویسندگان
چکیده
We proposed an approach for human pose estimation over monocular depth images. We augment the data by sampling from existing dataset and generate synthesized images. The generated dataset covers a more continuous pose space than the existing one. We use the generated dataset to train a multi-pathway neural network. We also introduced an orientation and translation invariant embedding for poses within the network.
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تاریخ انتشار 2016