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

תקציר מקורי באנגליתarXiv:2509.16727v5 Announce Type: replace-cross Abstract: 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