Existing deep learning-based human mesh reconstruction approaches have a tendency to
build larger networks to achieve higher accuracy. Computational complexity and model size …

We present a new method, called MEsh TRansfOrmer (METRO), to reconstruct 3D human
pose and mesh vertices from a single image. Our method uses a transformer encoder to …

We present a graph-convolution-reinforced transformer, named Mesh Graphormer, for 3D
human pose and mesh reconstruction from a single image. Recently both transformers and …

Traditional methods of reconstructing 3D human pose and mesh from single images rely on
paired image-mesh datasets, which can be difficult and expensive to obtain. Due to this …

In this paper, we decouple unsupervised human mesh recovery into the well-studied
problems of unsupervised 3D pose estimation, and human mesh recovery from estimated …

The end-to-end Human Mesh Recovery (HMR) approach has been successfully used for 3D
body reconstruction. However, most HMR-based frameworks reconstruct human body by …

Regression-based methods have recently shown promising results in reconstructing human
meshes from monocular images. By directly mapping raw pixels to model parameters, these …

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 …

We address the problem of regressing 3D human pose and shape from a single image with
a focus on 3D accuracy. The current best methods leverage large datasets of 3D pseudo …

Human mesh recovery from one single image has achieved rapid progress recently, but
many methods suffer from the image appearance overfitting since the training data are …