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.
@article{arxiv.2312.10195,
title = {SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation},
author = {David C. Jeong and Hongji Liu and Saunder Salazar and Jessie Jiang and Christopher A. Kitts},
journal= {arXiv preprint arXiv:2312.10195},
year = {2023}
}