PersonaAct: A Persona-Centric Dataset for Conversational Motion Generation

For 3D digital characters to perform expressively, their motion should reflect not only what they say and do, but also their personalities. However, datasets that link persona descriptions to conversational motion remain limited, making it difficult to learn and evaluate how characters express their personalities through motion. To address this issue, we first present PersonaAct, a multimodal 3D motion dataset with 7,542 clips covering 302 personas. It pairs persona profiles and motion descriptions with synchronized speech and motion across different dialogue scenarios. Second, we evaluate motion quality and alignment with persona profiles, speech, and motion descriptions. To assess whether the motion fits the intended character, we introduce a persona–motion matching metric. Third, experiments show that current methods struggle to balance multiple conditions, particularly persona profiles and motion descriptions. These findings highlight key challenge in persona-conditioned motion generation. PersonaAct provides data and evaluation tools for developing and testing methods that address these challenges. Dataset visualizations and examples are available at https://anonymous.4open.science/r/PersonaAct-ICLR.