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 Three-dimensional (3D) human pose estimation involves estimating the articulated
3D joint locations of a human body from an image or video. Due to its widespread …

Abstract Model-based 3D pose and shape estimation methods reconstruct a full 3D mesh for
the human body by estimating several parameters. However, learning the abstract …

Abstract We present Neural Articulated Radiance Field (NARF), a novel deformable 3D
representation for articulated objects learned from images. While recent advances in 3D …

Seniors who live alone at home are at risk of falling and injuring themselves and, thus, may
need a mobile robot that monitors and recognizes their poses automatically. Even though …

In neural networks, it is often desirable to work with various representations of the same
space. For example, 3D rotations can be represented with quaternions or Euler angles. In …

Vision-based monocular human pose estimation, as one of the most fundamental and
challenging problems in computer vision, aims to obtain posture of the human body from …

The graph convolutional network (GCN) has recently achieved promising performance of 3D
human pose estimation (HPE) by modeling the relationship among body parts. However …

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 …

Abstract We describe Human Mesh Recovery (HMR), an end-to-end framework for
reconstructing a full 3D mesh of a human body from a single RGB image. In contrast to most …