论文标题
H3WB：Human3.6M 3D全身数据集和基准测试
H3WB: Human3.6M 3D WholeBody Dataset and Benchmark
论文作者
论文摘要
我们为3D人类全身姿势估计提供了一个基准，其中涉及确定整个人体（包括面部，手，身体和脚）上精确的3D关键点。目前，缺乏完全注释且准确的3D全身数据集导致深网对特定身体部位进行训练，这些部位在推理过程中结合在一起。或者它们依赖于参数体模型提供的伪园真正图，这些模型不如基于检测的方法准确。为了克服这些问题，我们介绍了Human3.6M 3D全体（H3WB）数据集，该数据集使用可可全身布局为人类360万数据集提供了全体注释。 H3WB包括100K图像上的133个全身关键点注释，这是我们新的多视图管道使得成为可能的。我们还提出了三个任务：i）从2D完整的全身姿势提起3D全身姿势，ii）3D全身姿势从2D不完整的全身姿势提升，iii）3D全身姿势估计来自单个RGB图像。此外，我们向这些任务的流行方法报告了几个基线。此外，我们还提供了总捕捉的自动3D全身注释，并在实验上表明，与H3WB一起使用时，它有助于提高性能。代码和数据集可从https://github.com/wholebody3d/wholebody3d获得
We present a benchmark for 3D human whole-body pose estimation, which involves identifying accurate 3D keypoints on the entire human body, including face, hands, body, and feet. Currently, the lack of a fully annotated and accurate 3D whole-body dataset results in deep networks being trained separately on specific body parts, which are combined during inference. Or they rely on pseudo-groundtruth provided by parametric body models which are not as accurate as detection based methods. To overcome these issues, we introduce the Human3.6M 3D WholeBody (H3WB) dataset, which provides whole-body annotations for the Human3.6M dataset using the COCO Wholebody layout. H3WB comprises 133 whole-body keypoint annotations on 100K images, made possible by our new multi-view pipeline. We also propose three tasks: i) 3D whole-body pose lifting from 2D complete whole-body pose, ii) 3D whole-body pose lifting from 2D incomplete whole-body pose, and iii) 3D whole-body pose estimation from a single RGB image. Additionally, we report several baselines from popular methods for these tasks. Furthermore, we also provide automated 3D whole-body annotations of TotalCapture and experimentally show that when used with H3WB it helps to improve the performance. Code and dataset is available at https://github.com/wholebody3d/wholebody3d
