马尼德克斯特:通过连续对应嵌入和残余引导扩散进行手对象操纵合成
概括
马尼德克斯特 (ManiDext) 使用一种新的扩散框架,为对象操纵产生现实的手动. 它模拟了物体与手的接触对应关系,用于物理上可行的抓取和操纵任务.
科学领域:
- 机器人和人工智能 机器人和人工智能
- 计算机视觉和图形学
背景情况:
- 动态对象操纵需要同步的手动和对象轨迹来实现可信的交互.
- 精确建模手和物体之间的接触对应的准确建模对于灵巧的操纵至关重要.
研究的目的:
- 介绍ManiDext,一个统一的基于分层扩散的框架,用于生成手操纵和掌握姿势.
- 在机器人系统中实现无和物理可信的人物对象交互.
主要方法:
- 开发了一种连续对应嵌入式表示,用于详细的顶点级对象-手接触建模.
- 实现了手网嵌入的自我监督优化,反映了地理距离.
- 集成的代精制到扩散过程中,使用手工置残留物作为精制目标在denoising.
主要成果:
- 为各种操作任务生成物理可信和高度现实的动作.
- 在单手和双手抓手场景中取得了成功.
- 展示了对刚性和形物体的有效操纵.
结论:
- ManiDext提供了一个统一的框架,将生成和精细化合并为灵巧的手操纵.
- 拟议的连续对应嵌入和扩散中的代改进显著提高了运动现实性和可信性.
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