T. N. Schnabel, Y. Lill, B. K. Benitez, P. Nalabothu, P. Metzler, A. A. Mueller, M. Gross, B. Gözcü, B. Solenthaler
Medical Image Computing and Computer Assisted Intervention - MICCAI 2024 (Marrakesh, Morocco, October 06-10, 2024), pp. 217-227
Abstract
Learned 3-dimensional face models have emerged as valuable tools for statistically modeling facial variations, facilitating a wide range of applications in computer graphics, computer vision, and medicine. While these models have been extensively developed for adult faces, research on infant face models remains sparse, limited to a few models trained on small datasets, none of which are publicly available. We propose a novel approach to address this gap by developing a large-scale 3D INfant FACE model (INFACE) using a diverse set of face scans. By harnessing uncontrolled and incomplete data, INFACE surpasses previous efforts in both scale and accessibility. Notably, it represents the first publicly available shape model of its kind, facilitating broader adoption and further advancements in the field. We showcase the versatility of our learned infant face model through multiple potential clinical applications, including shape and appearance completion for mesh cleaning and treatment planning, as well as 3D face reconstruction from images captured in uncontrolled environments. By disentangling expression and identity, we further enable the neutralization of facial features---a crucial capability given the unpredictable nature of infant scanning.
