Respective Volumetric Heatmap Autoencoder for Multi-Person 3D Pose Estimation

Using heatmaps to predict body joint locations has become one of the best performing pose estimation methods, however, these methods often have the high demands for memory and computation, which make them difficult to apply into practice. This paper proposes an effective compression method to reduce the size of heatmaps, namely lies Respective Volumetric Heatmap Autoencoder(RVHA) to represent the ground truth heatmaps with smaller data size, then a RVHA-based pose estimation framework is built to achieve the human joint locations from monocular RGB images. Thanks to our compression strategy which takes each human joint volumetric heatmap as an input frame individually, our method performs favorably when compared to state of the art on the JTA datasets.