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 …

High-resolution dense prediction enables many appealing real-world applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …

Human pose is typically represented by a coordinate vector of body joints or their heatmap
embeddings. While easy for data processing, unrealistic pose estimates are admitted due to …

Recently, the vision transformer and its variants have played an increasingly important role
in both monocular and multi-view human pose estimation. Considering image patches as …

The objective of human pose estimation (HPE) derived from deep learning aims to
accurately estimate and predict the human body posture in images or videos via the …

Graph convolutional networks (GCNs) have brought remarkable progress in skeleton-based
action recognition. However, high computational cost and large model size make models …

This paper presents a novel Convolutional Neural Network (CNN) architecture for 2D human
pose estimation from RGB images that balances between high 2D human pose/skeleton …

Most current human pose estimation methods pursue excellent performance via large
models and intensive computational requirements, resulting in slower models. These …

This paper presents JAWS, an optimzation-driven approach that achieves the robust transfer
of visual cinematic features from a reference in-the-wild video clip to a newly generated clip …

Semantic segmentation enables many appealing realworld applications, such as
computational photography, autonomous driving, etc. However, the vast computational cost …