Human Pose Estimation from Sparse Inertial Measurements through Recurrent Graph Convolution. (arXiv:2107.11214v3 [cs.CV] UPDATED)

Web: http://arxiv.org/abs/2107.11214

Conventional methods for human pose estimation either require a high degree
of instrumentation, by relying on many inertial measurement units (IMUs), or
constraint the recording space, by relying on extrinsic cameras. These deficits
are tackled through the approach of human pose estimation from sparse IMU data.
We define adjacency adaptive graph convolutional long-short term memory
networks (AAGC-LSTM), to tackle human pose estimation based on six IMUs, while
incorporating the human body graph structure directly into the network. The
AAGC-LSTM combines …

arxiv convolution cv graph human