MICCAI 2022 Daily – Wednesday

“ We propose a deep learning-based approach, ” Pingkun tells us. “ FEM works because it can model the correspondence well between the bone and the facial tissue. Our method uses the attention mechanism to establish a correspondence, so that deep learning knows how the facial tissue moves correspondingly if a part of the bone moves. We transform the movement of the bone to the soft tissue. That is a major technical innovation. ” Xi will be presenting the work today - his first oral presentation paper. He describes the group’s method in more detail: “ First, we want to learn the spatial correspondence between bony and facial structures. We use PointNet++ networks to extract the structural features from the bony and facial point set. We learn the local spatial features from the facial and bony models and then compute their similarities. Each facial point has its corresponding bony points, and all the corresponding points have a weight to contribute to that specific facial point. There is a point-to-point correspondence matrix to transform the effect from the bone to the face . ” They use MLP to encode the bony movement into a local bony movement feature. Compared to previous methods, which encode the bony movements into a global vector that cannot be decoded locally, they encode it locally and decode it locally, using the just-learned correspondence matrix to transform the local bony movement into the local facial change. “ Using the correspondence matrix, we can transform the corresponding bony 7 DAILY MICCAI Wednesday Xi Fang