介绍ACASS:用于受控(e)运动感知研究的注释角色动画刺激集。

Frontiers in Robotics and AI Pub Date : 2019-09-27 eCollection Date: 2019-01-01 DOI:10.3389/frobt.2019.00094
Sebastian Lammers, Gary Bente, Ralf Tepest, Mathis Jording, Daniel Roth, Kai Vogeley
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引用次数: 4

摘要

其他人的动作告诉我们他们当前的活动以及他们的意图和情绪。由于缺乏合适的比较刺激材料,对动作识别和情绪推理的不同机制的研究一直受到限制。有问题的混淆可以来自低水平的物理特征(如亮度),也可以来自高水平的心理特征(如刺激难度)。在这里,我们提出了一个标准化的刺激数据集,它允许用相同的刺激来处理动作和情绪识别。该刺激集由792个基于全身动作捕捉协议的计算机动画和中性化身组成。研究人员对22名志愿者进行了动作捕捉,要求他们在三种不同的情绪(生气、高兴、悲伤)下进行六项日常活动(拖地、扫地、用滚筒画画、用刷子画画、擦拭、打磨)。每个运动协议的五秒剪辑使用两个虚拟摄像机视角为每个剪辑渲染成avi文件。与视频刺激相比,计算机动画可以使虚拟角色的外观标准化,并控制灯光和着色条件,从而减少刺激变化,使其仅为运动。为了控制刺激的低水平光学特征,我们开发并应用了一套MATLAB例程来提取刺激的基本物理特征,包括平均背景前景比例和逐帧像素变化动态。这些信息被用来识别异常值,并在行动和情绪类别中均匀化刺激。这导致了一个较小的刺激子集(792个片段数据库中的n = 83个动画),其中只包含两种不同的动作(拖地,扫地)和两种不同的情绪(生气,高兴)。为了根据心理标准进一步均匀化这一刺激子集,我们进行了一项在线观察者研究(N = 112参与者),以评估动作和情绪的识别率,这导致了数据库中32个片段的最终子选择(每个类别8个)。ACASS数据库及其子集为社会心理学、社会神经科学和沟通障碍的应用临床研究提供了独特的研究应用机会。所有792 avi文件,选定的子集,MATLAB代码,注释和动作捕捉数据(fbx文件)可在线获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Introducing ACASS: An Annotated Character Animation Stimulus Set for Controlled (e)Motion Perception Studies.

Introducing ACASS: An Annotated Character Animation Stimulus Set for Controlled (e)Motion Perception Studies.

Introducing ACASS: An Annotated Character Animation Stimulus Set for Controlled (e)Motion Perception Studies.

Introducing ACASS: An Annotated Character Animation Stimulus Set for Controlled (e)Motion Perception Studies.

Others' movements inform us about their current activities as well as their intentions and emotions. Research on the distinct mechanisms underlying action recognition and emotion inferences has been limited due to a lack of suitable comparative stimulus material. Problematic confounds can derive from low-level physical features (e.g., luminance), as well as from higher-level psychological features (e.g., stimulus difficulty). Here we present a standardized stimulus dataset, which allows to address both action and emotion recognition with identical stimuli. The stimulus set consists of 792 computer animations with a neutral avatar based on full body motion capture protocols. Motion capture was performed on 22 human volunteers, instructed to perform six everyday activities (mopping, sweeping, painting with a roller, painting with a brush, wiping, sanding) in three different moods (angry, happy, sad). Five-second clips of each motion protocol were rendered into AVI-files using two virtual camera perspectives for each clip. In contrast to video stimuli, the computer animations allowed to standardize the physical appearance of the avatar and to control lighting and coloring conditions, thus reducing the stimulus variation to mere movement. To control for low level optical features of the stimuli, we developed and applied a set of MATLAB routines extracting basic physical features of the stimuli, including average background-foreground proportion and frame-by-frame pixel change dynamics. This information was used to identify outliers and to homogenize the stimuli across action and emotion categories. This led to a smaller stimulus subset (n = 83 animations within the 792 clip database) which only contained two different actions (mopping, sweeping) and two different moods (angry, happy). To further homogenize this stimulus subset with regard to psychological criteria we conducted an online observer study (N = 112 participants) to assess the recognition rates for actions and moods, which led to a final sub-selection of 32 clips (eight per category) within the database. The ACASS database and its subsets provide unique opportunities for research applications in social psychology, social neuroscience, and applied clinical studies on communication disorders. All 792 AVI-files, selected subsets, MATLAB code, annotations, and motion capture data (FBX-files) are available online.

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