Estimating human pose and shape from monocular images is a long-standing problem in
computer vision. Since the release of statistical body models, 3D human mesh recovery has …

Human motion generation aims to generate natural human pose sequences and shows
immense potential for real-world applications. Substantial progress has been made recently …

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

The estimation of 3D human motion from video has progressed rapidly but current methods
still have several key limitations. First most methods estimate the human in camera …

Confronting the challenges of data scarcity and advanced motion synthesis in human-scene
interaction modeling we introduce the TRUMANS dataset alongside a novel HSI motion …

Learning-based methods have dominated the 3D human pose estimation (HPE) tasks with
significantly better performance in most benchmarks than traditional optimization-based …

Learning-based approaches to monocular motion capture have recently shown promising
results by learning to regress in a data-driven manner. However due to the challenges in …

Human motion synthesis is a fundamental task in computer animation. Recent methods
based on diffusion models or GPT structure demonstrate commendable performance but …

This paper introduces a novel human pose estimation approach using sparse inertial
sensors addressing the shortcomings of previous methods reliant on synthetic data. It …

DensePose provides a pixel-accurate association of images with 3D mesh coordinates but
does not provide a 3D mesh while Human Mesh Reconstruction (HMR) systems have high …