Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Abstract Human Action Recognition (HAR) involves human activity monitoring task in
different areas of medical, education, entertainment, visual surveillance, video retrieval, as …

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 …

Transformer encoder architectures have recently achieved state-of-the-art results on
monocular 3D human mesh reconstruction, but they require a substantial number of …

Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …

This paper addresses the problem of 3D human pose and shape estimation from a single
image. Previous approaches consider a parametric model of the human body, SMPL, and …

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …

Although significant improvement has been achieved recently in 3D human pose estimation,
most of the previous methods only treat a single-person case. In this work, we firstly propose …

State-of-the-art human pose estimation methods are based on heat map representation. In
spite of the good performance, the representation has a few issues in nature, such as non …