This paper presents Key2Mesh, a model that takes a set of 2D human pose
keypoints as input and estimates the corresponding body mesh. Since this
process does not involve any visual (i.e. RGB image) data, the model can be
trained on large-scale motion capture (MoCap) datasets, thereby overcoming the
scarcity of image datasets with 3D labels. To enable the model's application on
RGB images, we first run an off-the-shelf 2D pose estimator to obtain the 2D
keypoints, and then feed these 2D keypoints to Key2Mesh. To improve the
performance of our model on RGB images, we apply an adversarial domain
adaptation (DA) method to bridge the gap between the MoCap and visual domains.
Crucially, our DA method does not require 3D labels for visual data, which
enables adaptation to target sets without the need for costly labels. We
evaluate Key2Mesh for the task of estimating 3D human meshes from 2D keypoints,
in the absence of RGB and mesh label pairs. Our results on widely used H3.6M
and 3DPW datasets show that Key2Mesh sets the new state-of-the-art by
outperforming other models in PA-MPJPE for both datasets, and in MPJPE and PVE
for the 3DPW dataset. Thanks to our model's simple architecture, it operates at
least 12x faster than the prior state-of-the-art model, LGD. Additional
qualitative samples and code are available on the project website:
this https URL

提出了一个名为 Key2Mesh 的模型，使用 2D 人体姿势关键点作为输入并估计相应的身体网格。利用大规模的运动捕捉数据集对模型进行训练，克服了缺乏带有 3D 标签的图像数据集的问题。通过在 RGB 图像上应用对抗性领域自适应方法，提高了模型在 RGB 图像上的性能，无需昂贵的标签。在 H3.6M 和 3DPW 数据集上的结果表明，Key2Mesh 在 PA-MPJPE 方面超过其他模型，也在 MPJPE 和 PVE 方面超过了 3DPW 数据集中的其他模型。