The field of visual computing is rapidly advancing due to the emergence of generative
artificial intelligence (AI), which unlocks unprecedented capabilities for the generation …

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 …

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 …

Human motion modeling is important for many modern graphics applications, which typically
require professional skills. In order to remove the skill barriers for laymen, recent motion …

Abstract 3D human pose data collected in controlled laboratory settings present challenges
for pose estimators that generalize across diverse scenarios. To address this domain …

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 …

Recently, transformer-based methods have gained significant success in sequential 2D-to-
3D lifting human pose estimation. As a pioneering work, PoseFormer captures spatial …

We present a unified perspective on tackling various human-centric video tasks by learning
human motion representations from large-scale and heterogeneous data resources …

Recent transformer-based solutions have shown great success in 3D human pose
estimation. Nevertheless, to calculate the joint-to-joint affinity matrix, the computational cost …

Representing human performance at high-fidelity is an essential building block in diverse
applications, such as film production, computer games or videoconferencing. To close the …