Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction. One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks. There is little prior research on how to best supervise one model with such discrepant labels. We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons. As a remedy, we propose a novel affine-combining autoencoder (ACAE) method to perform dimensionality reduction on the number of landmarks. The discovered latent 3D points capture the redundancy among skeletons, enabling enhanced information sharing when used for consistency regularization. Our approach scales to an extreme multi-dataset regime, where we use 28 3D human pose datasets to supervise one model, which outperforms prior work on a range of benchmarks, including the challenging 3D Poses in the Wild (3DPW) dataset. Our code and models are available for research purposes.

本研究展示了一个基于深度学习的3D人体姿态估计方法，为了克服不同数据集提供的不同骨骼格式这一障碍，我们提出了一种新颖的仿射组合自编码器方法(ACAE)，以实现由多个数据集超级所见，其中我们使用28个3D人体姿态数据集来监督一个模型，并在各种基准测试中取得了比以前的工作更好的表现。

使用几何感知自编码器从数十个数据集中学习3D人体姿势估计，以桥接骨架格式之间的差异