However, as the recorded signals are sparse and quite noisy, online performance and global translation estimation turn out to be two key difficulties. In this paper, we present TransPose, a DNN-based approach to perform full motion capture (with both global translations and body poses) from only 6 Inertial Measurement Units (IMUs) at over 90 fps. Human36M is a well known human pose dataset that we have used in this work. It provides 3.6 milion video frames with labeled poses of 11 human subjects, performing 17 tasks/scenarios, recorded from 4 camera angles. This huge size of data makes it possible for deep networks to train effectively. In our present work, we restrict to using only. Fortunately for pose estimation there are couple of great Datasets available: COCO Keypoints challenge. MPII Human Pose Dataset. VGG Pose Dataset. The COCO model produces 18 points, while the MPII.

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