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 …

Accurate whole-body multi-person pose estimation and tracking is an important yet
challenging topic in computer vision. To capture the subtle actions of humans for complex …

We introduce a novel sequential modeling approach which enables learning a Large Vision
Model (LVM) without making use of any linguistic data. To do this we define a common …

We present an approach to reconstruct humans and track them over time. At the core of our
approach, we propose a fully" transformerized" version of a network for human mesh …

Although no specific domain knowledge is considered in the design, plain vision
transformers have shown excellent performance in visual recognition tasks. However, little …

We present InstructDiffusion a unified and generic framework for aligning computer vision
tasks with human instructions. Unlike existing approaches that integrate prior knowledge …

Being data-driven is one of the most iconic properties of deep learning algorithms. The birth
of ImageNet drives a remarkable trend of" learning from large-scale data" in computer vision …

We show, for the first time, that neural networks trained only on synthetic data achieve state-
of-the-art accuracy on the problem of 3D human pose and shape (HPS) estimation from real …

Top-down methods dominate the field of 3D human pose and shape estimation, because
they are decoupled from human detection and allow researchers to focus on the core …