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 …

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 …

Most of the current self-supervised representation learning (SSL) methods are based on the
contrastive loss and the instance-discrimination task, where augmented versions of the …

In this paper we introduce SMPLicit, a novel generative model to jointly represent body
pose, shape and clothing geometry. In contrast to existing learning-based approaches that …

Transformer architectures have achieved SOTA performance on the human mesh recovery
(HMR) from monocular images. However, the performance gain has come at the cost of …

Garment sewing pattern represents the intrinsic rest shape of a garment, and is the core for
many applications like fashion design, virtual try-on, and digital avatars. In this work, we …

Existing motion capture datasets are largely short-range and cannot yet fit the need of long-
range applications. We propose LiDARHuman26M, a new human motion capture dataset …

We propose a Transformer-based framework for 3D human texture estimation from a single
image. The proposed Transformer is able to effectively exploit the global information of the …

In monocular video 3D multi-person pose estimation, inter-person occlusion and close
interactions can cause human detection to be erroneous and human-joints grouping to be …

Human pose estimation has achieved significant progress on images with high imaging
resolution. However, low-resolution imagery data bring nontrivial challenges which are still …