Accurate pain expression synthesis is essential for improving clinical training and human-robot interaction. Current Robotic Patient Simulators (RPSs) lack realistic pain facial expressions, limiting their effectiveness in medical training. In this work, we introduce PainDiffusion, a generative model that synthesizes naturalistic facial pain expressions. Unlike traditional heuristic or autoregressive methods, PainDiffusion operates in a continuous latent space, ensuring smoother and more natural facial motion while supporting indefinite-length generation via diffusion forcing. Our approach incorporates intrinsic characteristics such as pain expressiveness and emotion, allowing for personalized and controllable pain expression synthesis. We train and evaluate our model using the BioVid HeatPain Database. Additionally, we integrate PainDiffusion into a robotic system to assess its applicability in real-time rehabilitation exercises. Qualitative studies with clinicians reveal that PainDiffusion produces realistic pain expressions, with a 31.2% ± 4.8% preference rate against ground-truth recordings. Our results suggest that PainDiffusion can serve as a viable alternative to real patients in clinical training and simulation, bridging the gap between synthetic and naturalistic pain expression.
Our methodology consists of three key components:
This section demonstrates the comparative performance of different approaches:
The videos showcase randomly sampled examples from our validation set, demonstrating the qualitative improvements achieved by our method.
Video Index: 1
This interactive demo showcases our model's ability to generate controlled facial expressions based on three key parameters:
Use the controls below to explore different combinations and observe how they affect the generated expressions.
@article{dam2024paindiffusion,
title={PainDiffusion: Can robot express pain?},
author={Quang Tien Dam and Tri Tung Nguyen Nguyen and Dinh Tuan Tran and Joo-Ho Lee},
year={2024},
url={https://arxiv.org/abs/2409.11635},
}