3D Human Pose Estimation from RGB+D Images with Convolutional Neural Networks

In this paper, we explore 3D human pose estimation on the RGB+D images. While many researchers try to directly predict 3D pose from single RGB image, we propose a simple framework that could predict 3D pose predictions with the RGB image and depth image. Our approach is based on two aspects. On the one hand, we predicted accurate 2D joint locations from RGB image by applying the stacked hourglass networks based on the improved residual architecture. On the other hand, in view of obtained 2D joint locations, we could estimate 3D pose with the depth after calculating depth image patches. In general, compared with the state-of-the-art approaches, our model achieves signification improvement on benchmark dataset.