基于Laban努力、形状和隐马尔可夫模型的情感运动生成
Affective Movement Generation using Laban Effort and Shape and Hidden Markov Models
摘要：
身体动作是一种重要的沟通媒介，通过它可以识别情感状态。传达情感的动作也可以赋予机器类似生命的属性，并有助于创建更具吸引力的人机交互。本文提出了一种自动情感运动生成的方法，利用两个运动抽象：1）拉班运动分析（LMA），2）隐马尔可夫模型。LMA提供了一个系统的工具，抽象表示运动的运动学和表现力的特点。给定一个期望的运动路径上的目标情感是要覆盖，所提出的方法搜索一个标记的数据集在LMA努力和形状空间类似的运动传递目标情感的期望的运动路径。获得所识别的运动的HMM抽象，并将其与期望的运动路径一起使用以生成新颖的运动，该新颖的运动是传达目标情感的期望的运动路径的调制版本。调制的程度可以变化，在生成的运动中的运动学和情感约束之间进行权衡。使用全身运动数据集测试所提出的方法。所提出的方法在生成可识别的目标情绪的运动的功效进行评估，使用一个验证的自动识别模型和用户研究。使用识别模型，从生成的动作中以72%的比率正确识别目标情绪。此外，用户研究中的参与者能够从生成的动作样本中正确感知目标情绪，尽管也观察到一些混淆的情况。
英文摘要：
Body movements are an important communication medium through which affective states can be discerned. Movements that convey affect can also give machines life-like attributes and help to create a more engaging human-machine interaction. This paper presents an approach for automatic affective movement generation that makes use of two movement abstractions: 1) Laban movement analysis (LMA), and 2) hidden Markov modeling. The LMA provides a systematic tool for an abstract representation of the kinematic and expressive characteristics of movements. Given a desired motion path on which a target emotion is to be overlaid, the proposed approach searches a labeled dataset in the LMA Effort and Shape space for similar movements to the desired motion path that convey the target emotion. An HMM abstraction of the identified movements is obtained and used with the desired motion path to generate a novel movement that is a modulated version of the desired motion path that conveys the target emotion. The extent of modulation can be varied, trading-off between kinematic and affective constraints in the generated movement. The proposed approach is tested using a full-body movement dataset. The efficacy of the proposed approach in generating movements with recognizable target emotions is assessed using a validated automatic recognition model and a user study. The target emotions were correctly recognized from the generated movements at a rate of 72% using the recognition model. Furthermore, participants in the user study were able to correctly perceive the target emotions from a sample of generated movements, although some cases of confusion were also observed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
