Recent transformer-based approaches have demonstrated excellent performance in 3D
human pose estimation. However, they have a holistic view and by encoding global …

Modern multi-layer perceptron (MLP) models have shown competitive results in learning
visual representations without self-attention. However, existing MLP models are not good at …

The current 3D human pose estimators face challenges in adapting to new datasets due to
the scarcity of 2D-3D pose pairs in target domain training sets. We present the Multi …

Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that
has yet to be thoroughly explored in existing research. To address this gap, we present …

Recent advancements in transformer-based methods have yielded substantial success in
2D-to-3D human pose estimation. Transformer-based estimators possess inherent …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

The estimation of 3D human poses from monocular videos presents a significant challenge.
The existing methods face the problems of deep ambiguity and self-occlusion. To overcome …

Transformers have been successfully applied in the field of video-based 3D human pose
estimation. However the high computational costs of these video pose transformers (VPTs) …

Abstract 3D Human Pose Estimation (3D-HPE) is a highly active and evolving research area
in computer vision with numerous applications such as extended reality, action recognition …

Accurately estimating the 3D pose of humans in video sequences requires both accuracy
and a well-structured architecture. With the success of transformers, we introduce the …