Learning-based 3D Infant Face and Head Model for Medical Application

Realistic 3D digital models of human heads and faces have the potential to transform current medical practices for pre-treatment diagnostics, treatment planning, patient documentation and quantitative assessment of treatments. In contrast to entertainment where 3D digital faces have become stunningly detailed and realistic with the help of artificial intelligence (AI), medical settings entail several challenges due to the small sizes of real patient data and under-representation of different age groups in the existing datasets.

In this project, we aim at building high-quality models of the human infant face and head learned from real multimodal datasets that derive from the medical treatment of infants. Using these models as priors, we will develop methods to reconstruct individual optimal target morphologies, which can be used as a reference in the treatment planning of infants with craniofacial malformations.