用符号语言解析人体动作，让运动表示更可解释、无歧义。
LingoMotion: An Interpretable and Unambiguous Symbolic Representation for Human Motion
- 借鉴语言层级结构，定义基于关节角的动作字母表
- 在Motion-X数据集上实现高保真动作表示
- 适合需要精准控制与理解动作的场景，如动画生成
现有动作表示方法（如MotionGPT）多为不可解释的隐向量，依赖关节位置易产生歧义。受自然语言层次结构（字母→词→短语→句子）启发，我们提出LingoMotion，一种可解释且无歧义的动作符号表示体系。本文设计了基于关节角的动作字母表，构建描述步行等简单动作及其速度、尺度等属性的词与短语形态，并建立用于描述复杂人类行为的语法结构。初步结果表明，在大规模动作数据集Motion-X上的实现与评估中，该表示具备高保真度。
原文摘要 · Abstract (English)
Existing representations for human motion, such as MotionGPT, often operate as black-box latent vectors with limited interpretability and build on joint positions which can cause ambiguity. Inspired by the hierarchical structure of natural languages - from letters to words, phrases, and sentences - we propose LingoMotion, a motion language that facilitates interpretable and unambiguous symbolic representation for both simple and complex human motion. In this paper, we introduce the concept design of LingoMotion, including the definitions of motion alphabet based on joint angles, the morphology for forming words and phrases to describe simple actions like walking and their attributes like speed and scale, as well as the syntax for describing more complex human activities with sequences of words and phrases. The preliminary results, including the implementation and evaluation of motion alphabet using a large-scale motion dataset Motion-X, demonstrate the high fidelity of motion representation.
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