通过生成式控制策略实现全身物体操作的灵活模拟。
MaskedManipulator: Versatile Whole-Body Manipulation
- 基于大规模动作捕捉数据训练生成控制策略。
- 支持用户指定目标物体或身体姿态等高层指令。
- 适用于交互式动画系统，超越特定任务限制。
我们解决在物理模拟下生成多样化全身物体操作动作的挑战。与以往专注于精细动作追踪、轨迹跟随或远程操控的方法不同，本框架允许用户指定多样化高层目标，如目标物体位姿或身体位姿。为此，我们提出MaskedManipulator，一种从大规模人体动作捕捉数据上训练的追踪控制器中提炼出的生成式控制策略。该两阶段学习过程使系统能够执行复杂交互行为，同时提供对角色和物体运动的直观控制。MaskedManipulator生成目标导向的操作行为，将交互式动画系统的应用范围扩展至任务专用解决方案之外。
原文摘要 · Abstract (English)
We tackle the challenges of synthesizing versatile, physically simulated human motions for full-body object manipulation. Unlike prior methods that are focused on detailed motion tracking, trajectory following, or teleoperation, our framework enables users to specify versatile high-level objectives such as target object poses or body poses. To achieve this, we introduce MaskedManipulator, a generative control policy distilled from a tracking controller trained on large-scale human motion capture data. This two-stage learning process allows the system to perform complex interaction behaviors, while providing intuitive user control over both character and object motions. MaskedManipulator produces goal-directed manipulation behaviors that expand the scope of interactive animation systems beyond task-specific solutions.
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