论文标题
多人的绝对3D人姿势估计，深度较弱
Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision
论文作者
论文摘要
在3D人类姿势估计中，最大的问题之一是缺乏大型，多样化的数据集。对于多人3D姿势估计，尤其如此，据我们所知，只有机器生成的注释可用于培训。为了减轻此问题，我们介绍了一个网络，该网络可以以弱监督的方式对其他RGB-D图像进行培训。由于存在廉价的传感器，因此具有深度图的视频可广泛使用，我们的方法可以利用一个大型，未经注释的数据集。我们的算法是一种单眼，多人，绝对姿势估计器。我们评估了几个基准测试的算法，显示错误率的一致提高。同样，我们的模型可以在Mupots-3D数据集上实现最新的结果。
In 3D human pose estimation one of the biggest problems is the lack of large, diverse datasets. This is especially true for multi-person 3D pose estimation, where, to our knowledge, there are only machine generated annotations available for training. To mitigate this issue, we introduce a network that can be trained with additional RGB-D images in a weakly supervised fashion. Due to the existence of cheap sensors, videos with depth maps are widely available, and our method can exploit a large, unannotated dataset. Our algorithm is a monocular, multi-person, absolute pose estimator. We evaluate the algorithm on several benchmarks, showing a consistent improvement in error rates. Also, our model achieves state-of-the-art results on the MuPoTS-3D dataset by a considerable margin.
