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：从运动和外表等外部因素估计心理特质是心理学中长期存在的挑战性问题，主要基于具身认知理论。迄今为止，该问题的研究通常使用配备侵入式身体传感器的私有小规模数据集。心理特质自动估计系统的潜在应用包括职业疲劳与心理状态评估、市场营销与广告等。本文提出PsyMo（步态心理特质）——一个新颖的多用途多模态数据集，旨在探索步态模式中呈现的心理线索。我们收集了312名受试者在7种不同行走变化和6种摄像机角度下的行走序列。参与者同时填写了6份心理问卷，共获取17项与人格、自尊、疲劳、攻击性和心理健康相关的心理测量属性。我们提出了两个心理特质估计评估协议。该数据集除用于从步态估计自我报告心理特质外，还可作为步态识别基准方法的即插即用替代方案。我们对所有与受试者身份相关的线索进行匿名化处理，仅公开发布轮廓图像、2D/3D人体骨架和3D SMPL人体网格模型。