Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment
This addresses the need for diverse and controllable data in automated pain assessment, particularly for non-communicative patients like those with dementia, though it is incremental in combining existing generative and transformer methods.
The paper tackles the problem of automated pain assessment from facial expressions by generating a large-scale synthetic dataset (3DPain) with 82,500 samples and introducing a Vision Transformer framework (ViTPain) that uses cross-modal distillation to enhance accuracy and interpretability.
Automated pain assessment from facial expressions is crucial for non-communicative patients, such as those with dementia. 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 present 3DPain, a large-scale synthetic dataset specifically designed for automated pain assessment, featuring unprecedented annotation richness and demographic diversity. Our three-stage framework generates diverse 3D meshes, textures them with diffusion models, and applies AU-driven face rigging to synthesize multi-view faces with paired neutral and pain images, AU configurations, PSPI scores, and the first dataset-level annotations of pain-region heatmaps. The dataset comprises 82,500 samples across 25,000 pain expression heatmaps and 2,500 synthetic identities balanced by age, gender, and ethnicity. We further introduce ViTPain, a Vision Transformer based cross-modal distillation framework in which a heatmap-trained teacher guides a student trained on RGB images, enhancing accuracy, interpretability, and clinical reliability. Together, 3DPain and ViTPain establish a controllable, diverse, and clinically grounded foundation for generalizable automated pain assessment.