  Computer vision has advanced significantly that many discriminative
approaches such as object recognition are now widely used in real applications.
We present another exciting development that utilizes generative models for the
mass customization of medical products such as dental crowns. In the dental
industry, it takes a technician years of training to design synthetic crowns
that restore the function and integrity of missing teeth. Each crown must be
customized to individual patients, and it requires human expertise in a
time-consuming and labor-intensive process, even with computer-assisted design
software. We develop a fully automatic approach that learns not only from human
designs of dental crowns, but also from natural spatial profiles between
opposing teeth. The latter is hard to account for by technicians but important
for proper biting and chewing functions. Built upon a Generative Adversar-ial
Network architecture (GAN), our deep learning model predicts the customized
crown-filled depth scan from the crown-missing depth scan and opposing depth
scan. We propose to incorporate additional space constraints and statistical
compatibility into learning. Our automatic designs exceed human technicians'
standards for good morphology and functionality, and our algorithm is being
tested for production use.
