全球和本地功能通信与变压器用于3D人类姿势估计
1Department of Electrical and Electronic Engineering, Hanyang University, Ansan, 15588, South Korea.
Scientific reports
|February 24, 2025
概括
通过引入全球变压器块来更好地捕捉和轨迹特征,GaLFormer增强了3D人类姿势估计. 这种新的方法改善了时空特征交换,以获得更准确的关节,形状和轨迹相关性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 时空变压器网络是3D人体姿势估计的最先进技术.
- 当前的方法往往限制了对框架或轨迹内的单个关节的关注,限制了全球特征通信.
研究的目的:
- 为了解决现有的变压器结构在3D人类姿势估计中的局限性.
- 提出一种新的架构,GaLFormer,它整合了本地和全球功能学习.
主要方法:
- 推出了GaLFormer,这是一个结合本地变压器块 (联合令牌) 和全球混合变压器块的网络.
- 全球块在定义的范围内的关节之间促进特征交换,强制执行诱导偏差.
- 交替重复的局部和全球块捕获关节,形状和轨迹之间的相关性.
主要成果:
- 在基准数据集上,GaLFormer表现出优越或具有竞争力的性能.
- 在人类3.6M,MPI-INF-3DHP和HumanEva数据集上进行评估.
- 在3D人体姿势估计方面取得了最先进的结果.
结论:
- GaLFormer有效地整合了本地和全球信息,以改进3D人类姿势估计.
- 拟议的架构通过考虑更广泛的时空环境来增强特征表示.
- 这项工作推进了基于变压器的模型在人类姿势分析中的功能.
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