Browsing by Subject "Laban movement analysis"

Now showing 1 - 4 of 4

Open Access
Conversational agent expressing ocean personality and emotions using laban movement analysis and nonverbal communication cues
(2019-08) Sonlu, Sinan
Conversational human characters are heavily used in computer animation to convey various messages. Appearance, movement and voice of such characters in uence their perceived personality. Analyzing different channels of human communication, including body language, facial expression and vocalics, it is possible to design animation that exhibit consistent personality. This would enhance the message and improve realism of the virtual character. Using OCEAN personality model, we design internal agent parameters that are mapped into movement and sound modi ers, which in turn produce the nal animation. Laban Movement Analysis and Nonverbal Communication Cues are used for the operations that output bone rotations and facial shape key values at each frame. Correlations between personality and spoken text, and relations between personality and vocal features are integrated to introduce compherensive agent behavior. Multiple animation modi cation algorithms and a personality based dialogue selection method is introduced. Resulting conversational agent is tested in different scenarios, including passport check and fastfood order. Using a speech to text API user controls the dialog ow. Recorded interactions are evaluated using Amazon Mechanical Turk. Multiple statements about agent personality are rated by the crowd. In each experiment, one personality parameter is set to an extreme while others remain neutral, expecting an effect on perception.
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 expression in cartoon animal characters using Sasang typology
(Wiley, 2023-05-01) Mailee, Hamila; Sonlu, Sinan; Güdükbay, Uğur
The movement style is an adequate descriptor of different personalities. While many studies investigate the relationship between apparent personality and high-level motion qualities in humans, similar research for animal characters still needs to be done. The variety in animals' skeletal configurations and texture complicates their pose estimation process. Our affect analysis framework includes a workflow for pose extraction in animal characters and a parameterization of the high-level animal motion descriptors inspired by Laban movement analysis. Using a data set of quadruped walk cycles, we prove the display of typologies in cartoon animal characters, reporting the point-biserial correlation between our motion parameters and the Sasang categories that reflect different personalities.
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.