BabyFlow：真实且富有表现力的婴儿面部的3D建模
BabyFlow: 3D modeling of realistic and expressive infant faces
AI总结：
BabyFlow通过归一化流和跨年龄表情迁移，实现婴儿面部的高精度3D建模与表情生成，提升早期发育障碍检测的准确性。
摘要：
早期检测发育障碍可以通过分析婴儿颅面形态来帮助，但建模婴儿面部具有挑战性，因为数据有限且表情频繁自发。我们介绍了BabyFlow，一种生成式AI模型，能够解耦面部身份和表情，从而独立控制两者。使用归一化流，BabyFlow学习了灵活的概率表示，能够捕捉表达婴儿面部复杂的非线性变化，而无需线性假设。为了解决稀缺且不受控的表情数据，我们进行了跨年龄表情迁移，将成人3D扫描的表情适应到婴儿数据集中，以丰富婴儿数据集，使其具有真实且系统的表情变体。结果，BabyFlow提高了3D重建的准确性，特别是在高表达区域如嘴、眼和鼻部，并支持合成和修改婴儿表情的同时保持身份。此外，通过与扩散模型集成，BabyFlow生成高保真的2D婴儿图像，具有一致的3D几何结构，为数据增强和早期面部分析提供了强大的工具。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
