用生成模型分离婴儿脸型与表情，实现精准可控的3D建模。
BabyFlow: 3D modeling of realistic and expressive infant faces
- 基于归一化流分解脸型与表情，独立控制二者。
- 在嘴、眼、鼻等区域重建精度显著提升，尤其在高表达时。
- 适合发育障碍早期筛查研究者使用，支持数据增强与图像生成。
早期发育障碍的检测可通过分析婴儿颅面形态实现，但因数据有限且表情自发性强，建模难度大。我们提出BabyFlow，一种生成式AI模型，可解耦面部身份与表情，实现独立控制。采用归一化流学习灵活的概率表示，捕捉婴儿面部复杂非线性变化，无需线性假设。为应对稀缺且无控的表情数据，引入跨年龄表情迁移，将成人3D扫描的表情迁移至婴儿数据，丰富具有真实性和系统性的表情变体。结果表明，BabyFlow显著提升3D重建精度，尤其在嘴、眼、鼻等高表达区域；同时支持表情合成与修改，保持身份一致性。结合扩散模型后，可生成具一致3D几何结构的高保真2D婴儿图像，为数据增强与早期面部分析提供强大工具。
原文摘要 · Abstract (English)
Early detection of developmental disorders can be aided by analyzing infant craniofacial morphology, but modeling infant faces is challenging due to limited data and frequent spontaneous expressions. We introduce BabyFlow, a generative AI model that disentangles facial identity and expression, enabling independent control over both. Using normalizing flows, BabyFlow learns flexible, probabilistic representations that capture the complex, non-linear variability of expressive infant faces without restrictive linear assumptions. To address scarce and uncontrolled expressive data, we perform cross-age expression transfer, adapting expressions from adult 3D scans to enrich infant datasets with realistic and systematic expressive variants. As a result, BabyFlow improves 3D reconstruction accuracy, particularly in highly expressive regions such as the mouth, eyes, and nose, and supports synthesis and modification of infant expressions while preserving identity. Additionally, by integrating with diffusion models, BabyFlow generates high-fidelity 2D infant images with consistent 3D geometry, providing powerful tools for data augmentation and early facial analysis.
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