用可执行程序表示人体动作，提升模型泛化与数据效率
Towards Human Motion World Models via Executable Behaviour Representations
- 设计专用语言ExAct，将动作表达为可编译的程序
- 零样本推理下实现动作分割与异常检测性能提升
- 适合需要跨任务迁移的运动建模研究者
人体动作世界模型应通过可执行性体现动作意图：能适应不同动作并评估动作质量。为此，我们提出一种领域特定语言ExAct，将人体动作表示为未完全定义的程序，可编译为奖励模型以实现零样本策略推断。利用ExAct程序的组合特性，将单个策略整合为可执行的行为表征。通过分析人体动作捕捉数据，评估该方法在人体动作分割与异常检测任务中的有效性。结果表明，其数据效率更高，且更准确捕捉动作间的直观关系，优于专用任务模型。
原文摘要 · Abstract (English)
Human motion world models should capture motion's intentionality by being executable: adaptable to different actions and capable of assessing motion quality. To achieve this, we introduce a domain-specific language ExAct that represents human motions as underspecified programs that can be compiled to a reward model for zero-shot policy inference. By leveraging the compositional nature of ExAct programs, we combine individual policies into executable behaviour representations. We evaluate the utility of the proposed approach by analysing human motion capture for the tasks of human action segmentation and human action anomaly detection. Our results suggest that the improvement in data efficiency and the capture of intuitive relationships between human actions are better than those of task-specific models.
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