对比式学习驱动的时空空间动态自适应框架,用于风格化的3D人类运动生成
Zhiqiang Song1, Ruyan Zhang2, Shuangjun Li1
1College of Physical Education, Shandong Sport University, Jinan, China.
PloS one
|February 19, 2026
概括
这项研究引入了一个新的框架,用于生成风格化的3D人类运动. 它通过使用对比学习和注意力机制,在动态运动中改进了当地风格的捕捉和细节.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 计算机图形 计算机图形
背景情况:
- 现有的3D人类运动生成方法往往忽视了当地的风格变化,导致生成的序列缺乏表现性的细节.
- 全球时间风格统计不足以捕捉动态人类运动的细微差别.
研究的目的:
- 为空间时空动态自适应风格化的3D人类运动生成提出一个对比的学习驱动框架.
- 为了增强捕捉当地风格变化的能力,并改善生成3D人类运动中的表达细节.
主要方法:
- 引入了空间注意力实例规范化 (SAIN) 和时间注意力实例规范化 (TAIN),以提取本地和全球运动风格特征.
- 采用双路径结构来隔离运动内容和风格注入器 (SADA,TADA) 来实现细粒度风格集成.
- 在培训期间利用了风格和内容的对比损失,以改善特征集群和分离.
主要成果:
- 拟议的方法在Xia数据集上取得了卓越的性能,FID为0.06,准确率为96.70%,多样性为5.67,多模式性为0.97,与真实数据密切匹配.
- 在运动风格转移任务中,该模型获得了94.11 CRA和89.41 SRA,超过了现有的最先进的方法.
结论:
- 开发的框架有效地解开了动作风格和内容,实现了细粒度,动态自适应的风格化3D人类运动生成.
- 对比式学习方法增强了风格的多样性和内容的真实性,产生了更具表现力和现实的人类动作.
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