Explicit Pose Deformation Learning for Tracking Human Poses
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We present a method for human pose tracking that learns explicitly about the dynamic effects of human motion on joint appearance. In contrast to previous techniques which employ generic tools such as dense optical flow or spatio-temporal smoothness constraints to pass pose inference cues between frames, our system instead learns to predict joint displacements from the previous frame to the current frame based on the possibly changing appearance of relevant pixels surrounding the corresponding joints in the previous frame. This explicit learning of pose deformations is formulated by incorporating concepts from human pose estimation into an optical flow-like framework. With this approach, state-of-the-art performance is achieved on standard benchmarks for various pose tracking tasks including 3D body pose tracking in RGB video, 3D hand pose tracking in depth sequences, and 3D hand gesture tracking in RGB video.
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