Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment

Automated pain assessment from facial expressions is crucial for non-communicative patient. Progress has been limited by two challenges: (i) existing datasets exhibit severe demographic and label imbalance due to ethical constraints, and (ii) current generative models cannot precisely control facial action units (AUs), facial structure, or clinically validated pain levels. We introduce 3DPain, a large-scale synthetic dataset designed to overcome data scarcity in automated pain assessment. Comprising 82,500 frames across 2,500 unique identities, 3DPain offers extensive heterogeneity in facial pain responses across demographic groups balanced by age, gender, and ethnicity. Our three-stage framework samples diverse 3D meshes, textures them with diffusion models, and applies AU-driven face rigging to synthesize multi-view faces with paired neutral/pain images, facial action units, PSPI scores, and pain-region heatmaps. We further introduce ViTPain, a Vision Transformer based framework leveraging cross-attention with a neutral reference face to achieve identityaware pain estimation. Together, 3DPain and ViTPain establish a controllable, diverse, and clinically grounded foundation for generalizable automated pain assessment.