Personality Perception in Human Videos Altered by Motion Transfer
  Networks
Ayda Yurtoğlu Sinan Sonlu Yalım Doğan Uğur Güdükbay
The successful portrayal of personality in digital characters improves
communication and immersion. Current research focuses on expressing personality
through modifying animations using heuristic rules or data-driven models. While
studies suggest motion style highly influences the apparent personality, the
role of appearance can be similarly essential. This work analyzes the influence
of movement and appearance on the perceived personality of short videos altered
by motion transfer networks. We label the personalities in conference video
clips with a user study to determine the samples that best represent the
Five-Factor model's high, neutral, and low traits. We alter these videos using
the Thin-Plate Spline Motion Model, utilizing the selected samples as the
source and driving inputs. We follow five different cases to study the
influence of motion and appearance on personality perception. Our comparative
study reveals that motion and appearance influence different factors: motion
strongly affects perceived extraversion, and appearance helps convey
agreeableness and neuroticism.