Multi-modal 3D Human Pose Estimation for Human-Robot Collaborative Applications

We propose a multi-modal 3D human pose estimation approach which combines a 2D human pose estimation network utilizing RGB data with a 3D human pose estimation network utilizing the 2D pose estimation results and depth information, in order to predict 3D human poses. We improve upon the state-of-the-art by proposing the use of a more accurate 2D human pose estimation network, as well as by introducing squeeze-excite blocks into the architecture of the 3D pose estimation network. More importantly, we focused on the challenging application of 3D human pose estimation during collaborative tasks. In that direction, we selected appropriate sub-sets that address collaborative tasks from a large-scale multi-view RGB-D dataset and generated a novel one-view RGB-D dataset for training and testing respectively. We achieved above state-of-the-art performance among RGB-D approaches when tested on a novel benchmark RGB-D dataset on collaborative assembly that we have created and made publicly available.