  While recent two-stage many-to-one deep learning models have demonstrated
great success in 3D human pose estimation, such models are inefficient ways to
detect 3D key points in a sequential video relative to one-shot and
many-to-many models. Another key drawback of two-stage and many-to-one models
is that errors in the first stage will be passed onto the second stage. In this
paper, we introduce SoloPose, a novel one-shot, many-to-many spatio-temporal
transformer model for kinematic 3D human pose estimation of video. SoloPose is
further fortified by HeatPose, a 3D heatmap based on Gaussian Mixture Model
distributions that factors target key points as well as kinematically adjacent
key points. Finally, we address data diversity constraints with the 3D
AugMotion Toolkit, a methodology to augment existing 3D human pose datasets,
specifically by projecting four top public 3D human pose datasets (Humans3.6M,
MADS, AIST Dance++, MPI INF 3DHP) into a novel dataset (Humans7.1M) with a
universal coordinate system. Extensive experiments are conducted on Human3.6M
as well as the augmented Humans7.1M dataset, and SoloPose demonstrates superior
results relative to the state-of-the-art approaches.
