  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.
