PainDiffusion: Can robot express pain?

Pain is a more intuitive and user-friendly way of communicating problems, making it especially useful in rehabilitation nurse training robots. While most previous methods have focused on classifying or recognizing pain expressions, we introduce PainDiffusion, a model that generates facial expressions in response to pain stimuli, with controllable expressiveness and emotion status. PainDiffusion utilizes diffusion forcing to roll out predictions over arbitrary lengths using a conditioned temporal U-Net. It operates as a latent diffusion model within EMOCA's facial expression latent space. For training, we process the BioVid Heatpain Database, extracting expression codes and subject identity configurations. We also propose a novel set of metrics to evaluate pain expressions, covering expressiveness, diversity, and the appropriateness of model-generated outputs. Finally, we demonstrate that PainDiffusion outperforms the autoregressive method, both qualitatively and quantitatively.