Gabriel Schneider | Searcharxiv

arXiv2026

LLM-Driven Personalities for Decision Making in Emergency Simulations

For virtual humans to appear believable, they must exhibit agency and spatial awareness while interacting with their environment in ways that reflect competence and intelligence. At the core of these capabilities lies effective decision-making, which strongly shapes agent behavior. With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have increasingly been explored as a mechanism to support such decision-making processes. In this work, we investigate the use of LLMs to drive decision-making in virtual humans within a simulated evacuation scenario, incorporating OCEAN personality traits into agent representations. Our goal is to evaluate how personality, expressed through language-based prompts, influences both individual behaviors and collective simulation outcomes. Our results demonstrate that LLM-driven personality profiles significantly impact agents' decisions, leading to distinct behavioral patterns across different traits. These findings suggest that heterogeneous crowds composed of LLM-guided agents can enhance the realism and variability of simulated environments, offering a flexible alternative to traditional rule-based approaches.

cs.GR↗

arXiv2024

Perceptual Analysis of Groups of Virtual Humans Animated using Interactive Platforms

Virtual humans (VH) have been used in Computer Graphics (CG) for many years, and perception studies have been applied to understand how people perceive them. Some studies have already examined how realism impacts the comfort of viewers. In some cases, the user's comfort is related to human identification. For example, people from a specific group may look positively at others from the same group. Gender is one of those characteristics that have in-group advantages. For example, in terms of VHs, studies have shown that female humans are more likely to recognize emotions in female VHs than in male VHs. However, there are many other variables that can impact the user perception. To aid this discussion, we conducted a study on how people perceive comfort and realism in relation to interactive VHs with different genders and expressing negative, neutral, or positive emotions in groups. We created a virtual environment for participants to interact with groups of VHs, which are interactive and should evolve in real-time, using a popular game engine. To animate the characters, we opted for cartoon figures that are animated by tracking the facial expressions of actors, using available game engine platforms to conduct the driven animation. Our results indicate that the emotion of the VH group impacts both comfort and realism perception, even by using simple cartoon characters in an interactive environment. Furthermore, the findings suggest that individuals reported feeling better with a positive emotion compared to a negative emotion, and that negative emotion recognition is impacted by the gender of the VHs group. Additionally, although we used simple characters, the results are consistent with the perception obtained when analysing realistic the state-of-the-art virtual humans, which positive emotions tend to be more correctly recognized than negative ones.

cs.HC↗