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Soft-tissue Driven Craniomaxillofacial Surgical Planning
Our framework consists of a bony planner network that estimates the bony movements required to achieve the desired facial outcome and a facial simulator network that can simulate the possible facial changes resulting from the estimated bony movement plans.
Deep Learning-based Facial Appearance Simulation Driven by Surgically Planned Craniomaxillofacial Bony Movement
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.
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