  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.
