用扩散模型生成逼真疼痛表情，提升医疗训练真实性
PainDiffusion: Learning to Express Pain
- 基于连续潜在空间的扩散模型，实现自然流畅的面部动态生成
- 在真实数据集上生成表情获临床专家31.2%偏好率，优于真实记录
- 支持个性化控制，适用于医疗仿真与机器人康复训练
准确的疼痛表情合成对提升临床培训和人机交互至关重要。现有机器人患者模拟器缺乏逼真的疼痛面部表现，限制了其在医学训练中的效果。本文提出PainDiffusion，一种生成自然疼痛面部表情的扩散模型。不同于传统启发式或自回归方法，PainDiffusion在连续潜在空间中运作，实现更平滑自然的面部运动，并通过扩散强制支持无限长度生成。模型融合疼痛表现力与情绪等内在特征，实现个性化、可控制的表情合成。我们在BioVid HeatPain数据库上训练并评估该模型，还将PainDiffusion集成至机器人系统，用于实时康复训练测试。临床专家的定性研究表明，PainDiffusion生成的表情有31.2%（标准差4.8%）被偏好于真实录音，证明其可作为临床训练中真实患者的可行替代方案，弥合合成与自然疼痛表达之间的差距。代码与视频见：https://damtien444.github.io/paindf/
原文摘要 · Abstract (English)
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% (std 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. Code and videos are available at: https://damtien444.github.io/paindf/
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