基于身体质量分布和重心约束的全身3D姿势估计
Fan Wei1, Guanghua Xu1,2,3,4, Qingqiang Wu1
1School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an 710049, China.
Sensors (Basel, Switzerland)
|July 12, 2025
概括
本研究介绍了一种基于变压器的方法,用于从单个图像中准确的3D人体姿势估计. 通过整合诸如重心之类的物理约束,它显著提高了关键点的准确性,特别是在具有挑战性的封闭场景中.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人的姿势估计 人的姿势估计
背景情况:
- 从单眼图像中估计3D人体姿势至关重要,但由于深度模两可和自我封闭而具有挑战性.
- 现有的方法往往缺乏足够的先验知识和强大的约束,导致不准确的3D关键点估计.
研究的目的:
- 使用变压器架构开发全身3D人体姿势估计的新方法.
- 通过整合物理约束来提高准确性和强度,特别是身体质量分布和重心.
主要方法:
- 变压器架构用于全身3D姿势估计.
- 该方法结合了解剖质量比和瞬间合成来计算分段级重心.
- 综合损失函数强制执行预测的关键点,重心和四肢长度不变性之间的一致性.
主要成果:
- 拟议的方法在人类3.6M全身数据集上实现了最先进的性能.
- 实现全身平均关节位置误差 (MPJPE) 为 44.49 毫米,比大简单基线提高 60.4%.
- 对身体部位的关键点显著减少MPJPE从112.6毫米降至40.41毫米,在封闭的场景中显示出增强的强度.
结论:
- 将物理约束,如重心,集成到深度学习框架中,可以提高3D姿势估计的准确性.
- 拟议的方法为全身3D姿势估计提供了强大而有效的解决方案,特别是在复杂和封闭的环境中.
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