机器学习方法用于3D运动合成和肌肉骨动力学的估计:一项调查
IEEE transactions on visualization and computer graphics
|August 25, 2023
概括
机器学习 (ML) 为3D人类运动分析和肌肉骨动态估计提供了更快,更实时的解决方案. 本综述对最近的ML进步进行了分类,指导了计算机图形和动画方面的未来研究.
科学领域:
- 计算机图形 计算机图形
- 生物力学 生物力学
- 机器学习 机器学习
背景情况:
- 传统的基于物理的方法3D人类运动和肌肉骨动力学是计算密集和不稳定的.
- 实时性能对于计算机图形应用至关重要,这是现有的基于物理的方法的局限性.
研究的目的:
- 审查和分类最近的机器学习 (ML) 技术用于人类运动预测,合成和肌肉骨动力学估计.
- 提供对最先进技术的洞察力,并确定人类运动分析的ML的未来研究方向.
- 为了突出基于ML的运动分析和肌肉骨动力学之间的协同联系,以增强角色动画.
主要方法:
- 在人类运动分析中对机器学习应用的文献综述和分类.
- 分析用于运动预测,运动合成和肌肉骨动力学估计的ML技术.
- 机器学习方法与传统基于物理的方法在计算效率和实时能力方面进行比较.
主要成果:
- 机器学习使代用模型能够最大限度地减少计算时间,并为运动分析提供近似的实时解决方案.
- 基于机器学习的肌肉骨动力学估计有助于通过自动化,快速的解决方案来建模长期的人体工程学效应.
- 通过ML集成运动分析和动态估计,提高了计算机图形中的自然角色动画.
结论:
- 机器学习是推进3D人类运动和肌肉骨动态分析的强大工具,与传统方法相比,它具有显著的优势.
- 本综述提供了ML应用的全面概述,为相关领域的新型研究和开发铺平了道路.
- 机器学习驱动的动作和动态分析之间的协同作用有望实现更现实的和高效的角色动画.
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