Aiming to address the problems of high computing costs and limited local receptive fields in
existing human pose estimation methods, this study proposes a novel framework for human …

Most recent methods formulate the task of human pose estimation as a heatmap estimation
problem, and use the overall L2 loss computed from the entire heatmap to optimize the …

Instance perception tasks (object detection, instance segmentation, pose estimation,
counting) play a key role in industrial applications of visual models. As supervised learning …

Text-driven person image generation is an emerging and challenging task in cross-modality
image generation. Controllable person image generation promotes a wide range of …

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 …

Human-centric perception (eg. detection, segmentation, pose estimation, and attribute
analysis) is a long-standing problem for computer vision. This paper introduces a unified …

This survey presents a comprehensive analysis of data augmentation techniques in human-
centric vision tasks, a first of its kind in the field. It delves into a wide range of research areas …

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

Human pose estimation on medium and small scales has long been a significant challenge
in this field. Most existing methods focus on restoring high-resolution feature maps by …

Hybrid models which combine the convolution and transformer model achieve impressive
performance on human pose estimation. However, the existing hybrid models on human …