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Journal of Peking University (Health Sciences) ›› 2026, Vol. 58 ›› Issue (1): 139-144. doi: 10.19723/j.issn.1671-167X.2026.01.018

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Method of constructing 3D facial smile simulation sequence data based on non-rigid registration

Aonan WEN1, Xiaohui ZHANG2, Yongtao YANG3, Zixiang GAO1, Wenbo LI3, Shenyao SHAN3, Xiangyi SHANG3, Yuwen TIAN3, Shuwei GUO1, Yizhen WANG3, Yong WANG1,3,*(), Yijiao ZHAO1,3,*()   

  1. 1. Center of Digital Dentistry, Department of Prosthodontics, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology, Beijing 100081, China
    2. National Center for Applied Mathematics, Academy for Multidisciplinary Studies, Capital Normal University, Beijing 100089, China
    3. Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
  • Received:2025-10-10 Online:2026-02-18 Published:2025-11-25
  • Contact: Yong WANG, Yijiao ZHAO
  • Supported by:
    the National Natural Science Foundation of China(82271039); the National Key Research and Development Program of China(2022YFC2405401); the Beijing Natural Science Foundation(L232100); the Beijing Natural Science Foundation(L242132); the Open Subject Foundation of Peking University School and Hospital of Stomatology(PKUSS20230201)

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Abstract:

Objective: To propose a novel method for constructing facial smile simulation sequence data based on static three-dimensional (3D) facial data captured at the start and end of smiling, and to preliminarily evaluate the accuracy and feasibility of the proposed method. Methods: The 3D dynamic facial data of participants transitioning from a neutral expression to a maximum smile were captured using the 3dMD dynamic facial scanning system. A structured 3D face template was deformed and registered to both the smile starting and ending facial data using the Procrustes analysis non-rigid iterative closest point (PA-NICP) registration algorithm developed by our research group, obtaining two sets of structured homologous data. In MATLAB software, the vertex displacements between the corresponding points of the starting and ending homologous datasets were calculated, and intermediate transitional data with a consistent triangular mesh topology were generated through linear interpolation, thereby constructing the facial smile simulation sequence data. The real 3D dynamic facial data captured from the 3dMD system were used as reference data, and the simulation sequence data constructed in this study were used as test data. The 3D morphological deviations between the reference and test data at multiple time points during the smiling process were calculated to evaluate the accuracy of the constructed smile simulation sequence data. Results: The 3D facial smile simulation sequence data were successfully constructed for one male and one female participants. The average 3D morphological deviation for the simulated sequence of the male participant was (0.31±0.04) mm, and the average 3D morphological deviation for the simulated sequence of the female participant was (0.44±0.08) mm. Conclusion: Based on the PA-NICP registration algorithm, the construction of facial smile simulation sequence data can be achieved. The intermediate transitional data can be parametrically generated and flexibly adjusted using interpolation functions, providing a novel method for 3D dynamic facial data generation that supports esthetic prosthodontic design, treatment outcome evaluation, and communication between clinicians and patients.

Key words: Esthetics, dental, Facial expression, Smiling, Three-dimensional imaging, Structured data, Homologous data, Virtual simulation

CLC Number: 

  • R783

Figure 1

Schematic diagram of dynamic facial data during the participant's smiling process A, the participant' s smile starting data; B-E, smile process data between starting data and ending data of participant' s smiles; F, the participant' s smile ending data."

Figure 2

Flowchart of the facial smile simulation sequence data construction method A, processed three-dimensional face template; B, the participant' s smile starting data; C, the participant' s smile ending data; D, structured smile starting data of participant (homologous data); E, structured smile ending data of participant (homologous data); F, smile simulation data obtained by linear interpolation. PA-NICP, Procrustes analysis non-rigid iterative closest point."

Figure 3

Three-dimensional morphological deviation of the constructed smile simulation sequence data A, smile process data between the participant' s smile starting and ending data; B, smile simulation data corresponding to data A; C, three-dimensional deviation chromatogram between data A and data B."

Figure 4

Schematic diagram of the construction effect of three-dimensional facial smile simulation sequence data of participants A, three-dimensional facial smile simulation sequence data of male participant; B, three-dimensional facial smile simulation sequence data of female participant."

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