Zobrazeno 1 - 10
of 21 120
pro vyhledávání: '"Human Pose Estimation"'
Autor:
Xin Wang1 wangx7988@sjzu.edu.cn, Guanhua Li2 2495823000@qq.com, Yongfeng Chen3 13513420447@163.com, Ge Wen4 2026678321@qq.com
Publikováno v:
Engineering Letters. Nov2024, Vol. 32 Issue 11, p2127-2137. 11p.
Human pose estimation (HPE) has received increasing attention recently due to its wide application in motion analysis, virtual reality, healthcare, etc. However, it suffers from the lack of labeled diverse real-world datasets due to the time- and lab
Externí odkaz:
http://arxiv.org/abs/2412.20538
Transformer-based methods have recently achieved significant success in 3D human pose estimation, owing to their strong ability to model long-range dependencies. However, relying solely on the global attention mechanism is insufficient for capturing
Externí odkaz:
http://arxiv.org/abs/2412.19676
Autor:
Scott, Bradley, de Vries, Clarisse, Durrant, Aiden, Oren, Nir, Chadwick, Edward, Blana, Dimitra
In this study, we investigated whether transfer learning from macaque monkeys could improve human pose estimation. Current state-of-the-art pose estimation techniques, often employing deep neural networks, can match human annotation in non-clinical d
Externí odkaz:
http://arxiv.org/abs/2412.15966
With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captured point clouds, robust human pose estimation remains challenging. Mos
Externí odkaz:
http://arxiv.org/abs/2412.13454
This paper explores the problem of 3D human pose estimation from only low-level acoustic signals. The existing active acoustic sensing-based approach for 3D human pose estimation implicitly assumes that the target user is positioned along a line betw
Externí odkaz:
http://arxiv.org/abs/2411.07165
Recent multi-frame lifting methods have dominated the 3D human pose estimation. However, previous methods ignore the intricate dependence within the 2D pose sequence and learn single temporal correlation. To alleviate this limitation, we propose TCPF
Externí odkaz:
http://arxiv.org/abs/2501.01770
Autor:
Purkrabek, Miroslav, Matas, Jiri
Current Human Pose Estimation methods have achieved significant improvements. However, state-of-the-art models ignore out-of-image keypoints and use uncalibrated heatmaps as keypoint location representations. To address these limitations, we propose
Externí odkaz:
http://arxiv.org/abs/2412.02254
Gait analysis using computer vision is an emerging field in AI, offering clinicians an objective, multi-feature approach to analyse complex movements. Despite its promise, current applications using RGB video data alone are limited in measuring clini
Externí odkaz:
http://arxiv.org/abs/2411.13716
Autor:
Yuan, Shizhe, Zhou, Li
With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex m
Externí odkaz:
http://arxiv.org/abs/2411.06725