Jiao Mingqi, Li Jiarui, Zhong Bingxu, Du Siyuan, Li Shuning, Zhang Manfei, Zhang Qibin, Liang Zhongming, Liu Fan, Zuo Chunman, Wang Sijia, Chen Luonan
Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 310024, China.
CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
Adv Sci (Weinh). 2025 Aug;12(29):e2414507. doi: 10.1002/advs.202414507. Epub 2025 May 7.
Facial morphology is a distinctive biometric marker, offering invaluable insights into personal identity, especially in forensic science. In the context of high-throughput sequencing, the reconstruction of 3D human facial images from DNA is becoming a revolutionary approach for identifying individuals based on unknown biological specimens. Inspired by artificial intelligence techniques in text-to-image synthesis, it proposes Difface, a multi-modality model designed to reconstruct 3D facial images only from DNA. Specifically, Difface first utilizes a transformer and a spiral convolution network to map high-dimensional Single Nucleotide Polymorphisms and 3D facial images to the same low-dimensional features, respectively, while establishing the association between both modalities in the latent features in a contrastive manner; and then incorporates a diffusion model to reconstruct facial structures from the characteristics of SNPs. Applying Difface to the Han Chinese database with 9,674 paired SNP phenotypes and 3D facial images demonstrates excellent performance in DNA-to-3D image alignment and reconstruction and characterizes the individual genomics. Also, including phenotype information in Difface further improves the quality of 3D reconstruction, i.e. Difface can generate 3D facial images of individuals solely from their DNA data, projecting their appearance at various future ages. This work represents pioneer research in de novo generating human facial images from individual genomics information.
面部形态是一种独特的生物特征标记，能为个人身份提供宝贵的见解，尤其是在法医学领域。在高通量测序的背景下，从DNA重建三维人类面部图像正成为一种基于未知生物样本识别个体的革命性方法。受文本到图像合成中的人工智能技术启发，研究提出了Difface，这是一种多模态模型，旨在仅从DNA重建三维面部图像。具体而言，Difface首先利用一个变换器和一个螺旋卷积网络，分别将高维单核苷酸多态性和三维面部图像映射到相同的低维特征，同时以对比的方式在潜在特征中建立两种模态之间的关联；然后结合一个扩散模型，根据单核苷酸多态性的特征重建面部结构。将Difface应用于包含9674对单核苷酸多态性表型和三维面部图像的汉族数据库，在DNA到三维图像对齐和重建方面表现出色，并对个体基因组学进行了表征。此外，在Difface中纳入表型信息进一步提高了三维重建的质量，即Difface可以仅根据个体的DNA数据生成其三维面部图像，预测其在不同未来年龄的外貌。这项工作代表了从个体基因组学信息从头生成人类面部图像的开创性研究。
