论文标题
Pose2mesh：3D人姿势的图形卷积网络和从2D人姿势中恢复的网状网络
Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose
论文作者
论文摘要
从输入图像中，大多数基于深度学习的3D人类姿势和网格估计方法都会回归人网格模型的姿势和形状参数，例如SMPL和Mano。这些方法的第一个弱点是出现域间隙问题，这是因为来自受控环境（例如实验室）的火车数据之间的图像外观不同，以及来自野外环境的测试数据。第二个弱点是，由于3D旋转的表示问题，姿势参数的估计非常具有挑战性。为了克服上述弱点，我们提出了Pose2mesh，这是一种基于新型的图形卷积神经网络（GraphCNN）的系统，该系统直接从2D人姿势估算人类网格顶点的3D坐标。第2D人的姿势作为输入提供了必不可少的人体表达信息，同时在两个域之间具有相对均匀的几何特性。同样，提出的系统避免了表示问题，同时使用粗到细的方式完全利用了网格拓扑。我们表明，我们的Pose2mesh在各种基准数据集上的前3D人姿势和网格估计方法胜过。有关代码，请参见https://github.com/hongsukchoi/pose2mesh_release。
Most of the recent deep learning-based 3D human pose and mesh estimation methods regress the pose and shape parameters of human mesh models, such as SMPL and MANO, from an input image. The first weakness of these methods is an appearance domain gap problem, due to different image appearance between train data from controlled environments, such as a laboratory, and test data from in-the-wild environments. The second weakness is that the estimation of the pose parameters is quite challenging owing to the representation issues of 3D rotations. To overcome the above weaknesses, we propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)-based system that estimates the 3D coordinates of human mesh vertices directly from the 2D human pose. The 2D human pose as input provides essential human body articulation information, while having a relatively homogeneous geometric property between the two domains. Also, the proposed system avoids the representation issues, while fully exploiting the mesh topology using a GraphCNN in a coarse-to-fine manner. We show that our Pose2Mesh outperforms the previous 3D human pose and mesh estimation methods on various benchmark datasets. For the codes, see https://github.com/hongsukchoi/Pose2Mesh_RELEASE.
