动态运动转换:用于人类姿势转换的混合数据驱动和模型驱动方法
概括
这项研究介绍了动态运动过渡 (DMT),这是一个混合方法,用于在3D动画中实现现实的人类姿势过渡. 它结合了数据驱动和模型驱动的方法,以改善运动合成和预测.
科学领域:
- 计算机图形和动画.
- 人工智能的人工智能
- 生物力学 生物力学
背景情况:
- 生成自然的人类姿势过渡对于3D图形和动画至关重要.
- 现有的基于模型的方法依赖于难以获得的启发式知识.
- 纯数据驱动的方法与未见的数据和有限的生成能力作斗争.
研究的目的:
- 为强大而准确的虚拟人类姿势过渡提出混合方法.
- 解决现有的纯数据驱动和模型驱动方法的局限性.
- 引入一种用于评估运动合成中的生成功率的新型指标.
主要方法:
- 开发了动态运动转换 (DMT),集成数据驱动和模型驱动的技术.
- 采用了使用物理定律和力衍生概念的数据增强.
- 利用条件时间变压器 (CTT) 进行微细级别的力变化学习.
- 应用动态运动原体 (DMP) 用于粗水平的运动创建.
主要成果:
- 拟议的混合方法的性能优于最先进的方法.
- 使用基于最小动作损失的新设计的指标实现了卓越的性能.
- 在产生现实的和真实的人类运动过渡方面表现出有效性.
结论:
- 动态运动转换 (DMT) 方法为虚拟人类动画提供了显著的优势.
- 混合方法提高了发电能力,并有效地处理数据限制.
- 这项工作有利于各种动画任务,包括运动合成,控制,跟踪和预测.
相关概念视频
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