  Simulating facial appearance change following bony movement is a critical
step in orthognathic surgical planning for patients with jaw deformities.
Conventional biomechanics-based methods such as the finite-element method (FEM)
are labor intensive and computationally inefficient. Deep learning-based
approaches can be promising alternatives due to their high computational
efficiency and strong modeling capability. However, the existing deep
learning-based method ignores the physical correspondence between facial soft
tissue and bony segments and thus is significantly less accurate compared to
FEM. In this work, we propose an Attentive Correspondence assisted Movement
Transformation network (ACMT-Net) to estimate the facial appearance by
transforming the bony movement to facial soft tissue through a point-to-point
attentive correspondence matrix. Experimental results on patients with jaw
deformity show that our proposed method can achieve comparable facial change
prediction accuracy compared with the state-of-the-art FEM-based approach with
significantly improved computational efficiency.
