Abstract:
This study aims to introduce a novel methodology for synthesizing emotional expressions in virtual characters, designed to adapt dynamic environmental transitions in real time, thereby enhancing interactive realism and responsiveness in locomotion systems. Emotions, which serve as a universal form of communication, profoundly impact human behaviour to drive instantaneous responses for all time scales of reality. Despite recent studies on motion synthesis, there is still an elusive connection between virtual characters and environments to explore as much as the fidelity needs to grow in both digital and virtual worlds. As a matter of fact, we put forth an innovative approach that leverages psychological frameworks to establish correlations between the character and its environment. This approach is reinforced by a novel deep-learning architecture designed to execute multiple modalities and tasks within a singular network concurrently. Replicating real-world properties in our work will lead to a fascinating initiative that attempts to give these virtual creations the ability to mimic, express, and evoke emotions in virtual environments. An observational study using standard motion synthesis criteria confirmed the system's effectiveness, revealing substantial improvements in performance and accuracy. Eventually, future approaches have been advocated in the context of multi-agent interactions to push the prevailing realism into the next generation. The study has determined that research aims were fulfilled within a challenging process, producing promising results for future applications in this research domain.
