Imbuing embodied agents with non-verbal behavior offers significant benefits for agent-human interactions. Despite extensive research on the creation of non-verbal behaviors, the field lacks a standardized benchmarking practice. Researchers rarely compare their findings with previous studies, and when they do, the comparisons are often not methodologically aligned. The GENEA Workshop 2025 aims to bring together the non-verbal behavior generation community to discuss major challenges and solutions in the field and determine the most effective ways to advance it.

Virtual agents with social characteristics such as warmth and competence, which are perceived readily from the face, impact human behavior effectively. However, generating such social signals is challenging because faces are complex, high-dimensional information spaces of 3D shape and complexion. We address this challenge using a data-driven psychology-based method that can objectively model socially impactful face features from subjective human perception. Specifically, we modeled the 3D shape and 2D complexion face features that drive four key social perceptions - competence, dominance, warmth, and trustworthiness. Analysis of the face models revealed that the four key traits are systematically structured by a set of latent face features including eyebrow height and chin protrusion, which can thus be used to efficiently generate social trait face features in virtual agents. We anticipate that our psychologically valid generative model of social face signals will enhance the social signaling capabilities of virtual agents.

Social robots must be able to generate realistic and recognizable facial expressions to engage their human users. Many social robots are equipped with standardized facial expressions of emotion that are widely considered to be universally recognized across all cultures. However, mounting evidence shows that these facial expressions are not universally recognized - for example, they elicit significantly lower recognition accuracy in East Asian cultures than they do in Western cultures. Therefore, without culturally sensitive facial expressions, state-of-the-art social robots are restricted in their ability to engage a culturally diverse range of human users, which in turn limits their global marketability. To develop culturally sensitive facial expressions, novel data-driven methods are used to model the dynamic face movement patterns that convey basic emotions (e.g., happy, sad, anger) in a given culture using cultural perception. Here, we tested whether such dynamic facial expression models, derived in an East Asian culture and transferred to a popular social robot, improved the social signalling generation capabilities of the social robot with East Asian participants. Results showed that, compared to the social robot's existing set of facial `universal' expressions, the culturally-sensitive facial expression models are recognized with generally higher accuracy and judged as more human-like by East Asian participants. We also detail the specific dynamic face movements (Action Units) that are associated with high recognition accuracy and judgments of human-likeness, including those that further boost performance. Our results therefore demonstrate the utility of using data-driven methods that employ human cultural perception to derive culturally-sensitive facial expressions that improve the social face signal generation capabilities of social robots. We anticipate that these methods will continue to inform the design of social robots and broaden their usability and global marketability.