生成可控合成人脸数据集，提升无语言患者疼痛评估的准确性
Pain in 3D: Generating Controllable Synthetic Faces for Automated Pain Assessment
- 用三维网格+扩散模型生成带面部动作单元控制的合成人脸
- 构建8.25万帧、2500个身份的均衡数据集，含疼痛评分与热力图
- 适合医疗AI研究者，尤其关注可解释性与临床落地的团队
从面部表情自动评估疼痛对无法沟通患者至关重要。现有研究受限于两大挑战：(i) 数据集因伦理限制存在严重人口统计与标签失衡；(ii) 当前生成模型无法精确控制面部动作单元（AUs）、面部结构或临床验证的疼痛水平。本文提出3DPain，一个大规模合成数据集，旨在解决自动化疼痛评估中的数据稀缺问题。该数据集包含2500个唯一身份、共82,500帧，覆盖跨年龄、性别和种族的多样化疼痛反应。我们的三阶段框架首先采样多样化的3D网格，利用扩散模型进行纹理渲染，并通过基于面部动作单元的骨骼绑定生成多视角图像，每张图像均配有中性/疼痛对比图、面部动作单元标注、PSPI评分及疼痛区域热力图。我们进一步提出ViTPain，一种基于视觉变压器的框架，通过交叉注意力机制引入中性参考面部实现身份感知的疼痛估计。3DPain与ViTPain共同构建了一个可控、多样且临床可信的通用自动化疼痛评估基础。
原文摘要 · Abstract (English)
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 them with diffusion models, and applies AU-driven face rigging to synthesize multi-view faces with paired neutral/pain images, facial action units, PSPI scores, and pain-region heatmaps. We further introduce ViTPain, a Vision Transformer based framework leveraging cross-attention with a neutral reference face to achieve identityaware pain estimation. Together, 3DPain and ViTPain establish a controllable, diverse, and clinically grounded foundation for generalizable automated pain assessment.
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