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Found 4 papers, 1 papers with code
Convolutional Pose Machines
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models.
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Predicting Multiple Structured Visual Interpretations
We present a simple approach for producing a small number of structured visual outputs which have high recall, for a variety of tasks including monocular pose estimation and semantic scene segmentation.
User-Specific Hand Modeling from Monocular Depth Sequences
We focus on modeling the human hand, and assume that a single rough template model is available.
Tracking Human Pose by Tracking Symmetric Parts
In this work, we present an occlusion aware algorithm for tracking human pose in an image sequence, that addresses the problem of double counting.
