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.

本文介绍了SoloPose，一种新颖的一次性、多对多的时空转换模型，用于视频中的运动学3D人体姿势估计。SoloPose进一步加强了HeatPose，一种基于高斯混合模型分布的3D热图，它对目标关键点和动力学相邻关键点进行因素分解。最后，我们通过3D AugMotion工具包解决了数据多样性限制问题，该方法通过将四个顶级公共3D人体姿势数据集（Humans3.6M、MADS、AIST Dance++、MPI INF 3DHP）投影到一个新的数据集（Humans7.1M）中，使用了一个通用的坐标系统。在Human3.6M和增强的Humans7.1M数据集上进行了大量实验证明，SoloPose相对于最先进的方法具有优越的结果。

SoloPose: 基于视频数据增强的一次性运动学 3D 人体姿势估计