Psychological trait estimation from external factors such as movement and appearance is a challenging and long-standing problem in psychology, and is principally based on the psychological theory of embodiment. To date, attempts to tackle this problem have utilized private small-scale datasets with intrusive body-attached sensors. Potential applications of an automated system for psychological trait estimation include estimation of occupational fatigue and psychology, and marketing and advertisement. In this work, we propose PsyMo (Psychological traits from Motion), a novel, multi-purpose and multi-modal dataset for exploring psychological cues manifested in walking patterns. We gathered walking sequences from 312 subjects in 7 different walking variations and 6 camera angles. In conjunction with walking sequences, participants filled in 6 psychological questionnaires, totalling 17 psychometric attributes related to personality, self-esteem, fatigue, aggressiveness and mental health. We propose two evaluation protocols for psychological trait estimation. Alongside the estimation of self-reported psychological traits from gait, the dataset can be used as a drop-in replacement to benchmark methods for gait recognition. We anonymize all cues related to the identity of the subjects and publicly release only silhouettes, 2D / 3D human skeletons and 3D SMPL human meshes.

翻译：从运动、外貌等外部因素估计心理特征是心理学中一个具有挑战性且长期存在的问题，主要基于具身心理理论。迄今为止，针对该问题的研究多采用侵入式身体附着传感器采集的私有小规模数据集。心理特征自动估计系统的潜在应用包括职业疲劳与心理评估、市场营销与广告。本文提出PsyMo（运动中心理特征），这是一个新颖、多用途、多模态的数据集，旨在探索步态模式中呈现的心理线索。我们采集了312名受试者在7种不同行走状态和6种摄像机角度下的行走序列。除行走序列外，受试者还填写了6份心理问卷，共涵盖17项心理测量属性，涉及人格、自尊、疲劳、攻击性与心理健康。我们提出了两种心理特征估计评价协议。除基于步态估计自我报告心理特征外，该数据集还可作为替代方案用于基准化步态识别方法。我们匿名化了所有与受试者身份相关的信息，仅公开发布剪影、二维/三维人体骨架与三维SMPL人体网格。