Disentangling human dynamics for pedestrian locomotion forecasting with noisy supervision
Abstract:
A method for predicting spatial positions of several key points on a human body in the near future in an egocentric setting is described. The method includes generating a frame-level supervision for human poses. The method also includes suppressing noise and filling missing joints of the human body using a pose completion module. The method further includes splitting the poses into a global stream and a local stream. Furthermore, the method includes combining the global stream and the local stream to forecast future human locomotion.
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