Evaluating the quality of generated nonverbal behavior remains a major challenge in the development of generative models.While human evaluations are reliable, they are costly and impractical for large-scale or iterative optimization.In this work, we propose an objective evaluation framework based on aggregated ranks across multiple fidelity and diversity metrics, computed from both raw features and learned latent representations.Compared to existing works, our framework emphasizes consistency across multiple metrics, aiming to provide a more holistic assessment.

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.

Research on non-verbal behavior generation for social interactive agents focuses mainly on the believability and synchronization of non-verbal cues with speech. However, existing models, predominantly based on deep learning architectures, often perpetuate biases inherent in the training data. This raises ethical concerns, depending on the intended application of these agents. This paper addresses these issues by first examining the influence of gender on facial non-verbal behaviors. We concentrate on gaze, head movements, and facial expressions. We introduce a classifier capable of discerning the gender of a speaker from their non-verbal cues. This classifier achieves high accuracy on both real behavior data, extracted using state-of-the-art tools, and synthetic data, generated from a model developed in previous work. Building upon this work, we present a new model, FairGenderGen, which integrates a gender discriminator and a gradient reversal layer into our previous behavior generation model. This new model generates facial non-verbal behaviors from speech features, mitigating gender sensitivity in the generated behaviors. Our experiments demonstrate that the classifier, developed in the initial phase, is no longer effective in distinguishing the gender of the speaker from the generated non-verbal behaviors.

Non-verbal behavior offers significant benefits for embodied agents in 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 aligned with other methodologies. The GENEA Workshop 2024 aims to unite the community to discuss major challenges and solutions, and to determine the most effective ways to advance the field.