This paper presents a simple yet powerful method for 3D human mesh reconstruction from a single RGB image. Most recently, the non-local interactions of the whole mesh vertices have been effectively estimated in the transformer while the relationship between body parts also has begun to be handled via the graph model. Even though those approaches have shown the remarkable progress in 3D human mesh reconstruction, it is still difficult to directly infer the relationship between features, which are encoded from the 2D input image, and 3D coordinates of each vertex. To resolve this problem, we propose to design a simple feature sampling scheme. The key idea is to sample features in the embedded space by following the guide of points, which are estimated as projection results of 3D mesh vertices (i.e., ground truth). This helps the model to concentrate more on vertex-relevant features in the 2D space, thus leading to the reconstruction of the natural human pose. Furthermore, we apply progressive attention masking to precisely estimate local interactions between vertices even under severe occlusions. Experimental results on benchmark datasets show that the proposed method efficiently improves the performance of 3D human mesh reconstruction. The code and model are publicly available at: https://github.com/DCVL-3D/PointHMR_release.

翻译：本文提出一种简洁而强大的方法，用于从单张RGB图像重建三维人体网格。近年来，Transformer有效估计了整个网格顶点的非局部交互，而图模型也开始处理身体部位之间的关系。尽管这些方法在三维人体网格重建中取得了显著进展，但直接推断从二维输入图像编码的特征与每个顶点三维坐标之间的关系仍然困难。为解决这一问题，我们设计了一个简单的特征采样方案。其核心思想是通过跟随点的引导（即三维网格顶点投影结果的估计值）在嵌入空间中进行特征采样。这有助于模型更关注二维空间中与顶点相关的特征，从而重建出自然的人体姿态。此外，我们应用渐进式注意力掩码，即使在严重遮挡情况下也能精确估计顶点间的局部交互。基准数据集上的实验结果表明，该方法有效提升了三维人体网格重建的性能。代码和模型已在 https://github.com/DCVL-3D/PointHMR_release 公开。