no code implementations • 20 Jul 2023 • Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Nathan Lampen, Jungwook Lee, Hannah H. Deng, Jaime Gateno, Michael A. K. Liebschner, James J. Xia, Pingkun Yan
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.
no code implementations • 4 Oct 2022 • Xi Fang, Daeseung Kim, Xuanang Xu, Tianshu Kuang, Hannah H. Deng, Joshua C. Barber, Nathan Lampen, Jaime Gateno, Michael A. K. Liebschner, James J. Xia, Pingkun Yan
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.
1 code implementation • 5 Apr 2022 • Xuanang Xu, Hannah H. Deng, Tianyi Chen, Tianshu Kuang, Joshua C. Barber, Daeseung Kim, Jaime Gateno, James J. Xia, Pingkun Yan
In this paper, we first conduct a theoretical analysis on the FL algorithm to reveal the problem of model aggregation during training on non-iid data.
no code implementations • 11 Sep 2021 • Deqiang Xiao, Hannah Deng, Tianshu Kuang, Lei Ma, Qin Liu, Xu Chen, Chunfeng Lian, Yankun Lang, Daeseung Kim, Jaime Gateno, Steve Guofang Shen, Dinggang Shen, Pew-Thian Yap, James J. Xia
In the training stage, the simulator maps jaw deformities of a patient bone to a normal bone to generate a simulated deformed bone.