论文标题
可学习的3D人姿势和形状估计的人网三角剖分
Learnable human mesh triangulation for 3D human pose and shape estimation
论文作者
论文摘要
与关节位置相比，在皮肤多人线性模型（SMPL）基于多视图图像的基于皮肤的多人线性模型（SMPL）的人网格重建中，关节旋转和形状估计的准确性相对较少。该领域的工作大致分为两类。第一种方法执行关节估计，然后通过将SMPL拟合到由此产生的接头来产生SMPL参数。第二种方法通过基于卷积神经网络（CNN）模型直接从输入图像中回归SMPL参数。但是，这些方法缺乏解决联合旋转和形状重建和网络学习难度的歧义的信息。为了解决上述问题，我们提出了一种两阶段的方法。提出的方法首先通过从输入图像中的基于CNN的模型估算网格顶点的坐标，并通过将SMPL模型拟合到估计的顶点来获取SMPL参数。估计的网格顶点提供了足够的信息来确定关节旋转和形状，并且比SMPL参数更容易学习。根据使用Human 36M和MPI-INF-3DHP数据集的实验，该提出的方法在关节旋转和形状估计方面显着优于先前的作品，并在关节位置估计方面实现了竞争性能。
Compared to joint position, the accuracy of joint rotation and shape estimation has received relatively little attention in the skinned multi-person linear model (SMPL)-based human mesh reconstruction from multi-view images. The work in this field is broadly classified into two categories. The first approach performs joint estimation and then produces SMPL parameters by fitting SMPL to resultant joints. The second approach regresses SMPL parameters directly from the input images through a convolutional neural network (CNN)-based model. However, these approaches suffer from the lack of information for resolving the ambiguity of joint rotation and shape reconstruction and the difficulty of network learning. To solve the aforementioned problems, we propose a two-stage method. The proposed method first estimates the coordinates of mesh vertices through a CNN-based model from input images, and acquires SMPL parameters by fitting the SMPL model to the estimated vertices. Estimated mesh vertices provide sufficient information for determining joint rotation and shape, and are easier to learn than SMPL parameters. According to experiments using Human3.6M and MPI-INF-3DHP datasets, the proposed method significantly outperforms the previous works in terms of joint rotation and shape estimation, and achieves competitive performance in terms of joint location estimation.
