  We present a software that predicts non-cleft facial images for patients with
cleft lip, thereby facilitating the understanding, awareness and discussion of
cleft lip surgeries. To protect patients privacy, we design a software
framework using image inpainting, which does not require cleft lip images for
training, thereby mitigating the risk of model leakage. We implement a novel
multi-task architecture that predicts both the non-cleft facial image and
facial landmarks, resulting in better performance as evaluated by surgeons. The
software is implemented with PyTorch and is usable with consumer-level color
images with a fast prediction speed, enabling effective deployment.
