Inverse Kinematics (IK) provides control over animation, facilitating the creation of full-body poses by utilizing target end-effector locations. Many approaches address the physical aspects of arranging limb configurations; however, systems that consider the psychological aspects of human motion are lacking. To this end, we introduce a quantitative translation of the qualitative concepts of Laban Movement Analysis (LMA) into computable, continuous style descriptors. Building upon this formulation, we also propose a data-driven Inverse Kinematics (IK) method that directly utilizes these LMA parameters to refine generated animations. Specifically, we refer to LMA Shape Qualities and the attitude towards the Kinesphere to control the orientation of the generated pose along the vertical and horizontal axes. Our Interpolator upsamples sparse end-effector keyframes into dense paths and modulates Time Effort at the trajectory level. Flow Effort is controlled by a pose-similarity objective that deliberately reduces pose similarity to the dataset examples. Through a perception user study, we show that the system can successfully apply LMA-based changes to the motion to express different personality traits. This data-driven system can ease the process of controlling the psychological aspect of generative animation.

This work investigates the effects of personality expression and embodiment in conversational agents. We extend a personality-driven conversational agent framework by integrating LLM-based conversation support to provide information about contemporary scientific topics. We describe a user study built on this system to evaluate two opposing personality styles using three models: a dialogue-only model that conveys personality verbally, an animated human model that expresses personality only through dialogue, and an animated human model expressing personality through dialogue and expressive animations. The users perceive all models positively regarding personality and learning outcomes; however, models with high personality traits are perceived as more engaging than those with low personality traits. We provide an analysis of personality perception, learning, and user experience.

ABSTRACT Large Language Models (LLMs) offer almost immediate human‐like quality responses to user queries. Conversational agent systems support natural language dialogues utilizing LLM backends in combination with Text‐to‐Speech (TTS) and Automatic Speech Recognition (ASR) technologies, enabling life‐like characters in virtual environments. This study investigates the relationship between user personality and perceived agent personality in LLM‐based natural language dialogue. We adopt a Virtual Reality (VR) setting where the user can talk with the agent that assumes the role of Socrates, the famous philosopher. To this end, we utilize a three‐dimensional (3D) avatar model resembling Socrates and use specific LLM prompts to get stylistic answers from OpenAI's Chat Completions Application Programming Interface (API). Our user study measures the agent's personality and the system's ease of use, quality, realism, and immersion concerning the user's self‐reported personality. The results suggest that the user's conscientiousness, extraversion, and emotional stability have a moderate effect on certain personality factors and system qualities. User conscientiousness affects the perceived ease of use, quality, and realism, while user extraversion affects perceived agent conscientiousness, system realism, and immersion. Additionally, the user's emotional stability correlates with perceived extraversion and agreeableness.

We express our personality through verbal and nonverbal behavior. While verbal cues are mostly related to the semantics of what we say, nonverbal cues include our posture, gestures, and facial expressions. Appropriate expression of these behavioral elements improves conversational virtual agents’ communication capabilities and realism. Although previous studies focus on co-speech gesture generation, they do not consider the personality aspect of the synthesized animations. We show that automatically generated co-speech gestures naturally express personality traits, and heuristics-based adjustments for such animations can further improve personality expression. To this end, we present a framework for enhancing co-speech gestures with the different personalities of the Five-Factor model. Our experiments suggest that users perceive increased realism and improved personality expression when combining heuristics-based motion adjustments with co-speech gestures.

Abstract 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.

Indoor positioning is one of the difficult problems in current navigation systems. There is an increasing demand for detecting the locations of objects and humans inside closed environments in various fields including surveillance, robotics, and entertainment. Recent works focus on indoor navigation systems using different technologies including Wireless Local Area Network (WLAN), Radio Frequency Identification (RFID), Inertial Measurement Unit (IMU), and Simultaneous Localization and Mapping (SLAM). Research reveals using these technologies alone is inefficient in terms of accuracy and cost. To address this issue, we propose a marker-based Augmented Reality (AR) indoor navigation system with integrated SLAM and IMU. We use Unity's AR Foundation Framework for highly accurate results with minimum hardware requirements.