Browsing by Author "Ergüzen, Arçin Ülkü"

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Open Access
Human movement personality detection parameters
(IEEE, 2024-06-23) Sonlu, Sinan; Dogan, Yalım; Ergüzen, Arçin Ülkü; Ünalan, Musa Ege; Demirci, Serkan; Durupinar, Funda; Güdükbay, Uğur
In this study, we develop a system that detects apparent personality traits from animation data containing human movements. Since the datasets that can be used for this purpose lack sufficient variance, we determined labels for the samples in two datasets containing human animations, in terms of the Five Factor Personality Theory, with the help of a user study. Using these labels, we identified movement parameters highly dependent on personality traits and based on Laban Movement Analysis categories. The artificial neural networks we trained for personality analysis from animation data show that models that take the motion parameters determined in the study as input have a higher accuracy rate than models that take raw animation data as input. Therefore, using the parameters determined in this study to evaluate human movements in terms of their personality traits will increase the systems' success.
Open Access
Personality transfer in human animation: comparing handcrafted and data-driven approaches
(2024-09) Ergüzen, Arçin Ülkü
The ability to perceive and alter personality traits in animation has significant implications for fields such as character animation and interactive media. Research and developments that use systematic tools or machine learning approaches show that personality can be perceived from different modalities such as audio, images, videos, and motions. Traditionally, handcrafted frameworks have been used to modulate motion and alter perceived personality traits. However, deep learning approaches also offer the potential for more nuanced and automated personality augmentation than handcrafted approaches. To address this evolving landscape, we compare the efficacy of handcrafted models with deep-learning models in altering perceived personality traits in animations. We examined various approaches for personality recognition, motion alteration, and motion generation. We developed two methods for modulating motions to alter OCEAN personality traits based on our findings. The first method is a handcrafted tool that modifies bone positions and rotations using Laban Movement Analysis (LMA) parameters. The second method involves a deep-learning model that separates motion content from personality traits. We could change the overall animation by altering the personality traits through this model. These models are evaluated through a three-part user study, revealing distinct strengths and limitations in both approaches.