<![CDATA[Motion capture is a popular research topic, with one of its main applications being human face reconstruction. The demand for converting 2D images into 3D reconstructions continues to increase, especially in facial reconstruction, where progress is made in improving the accuracy of facial position prediction. However, there is still a significant gap in developing facial reconstruction technologies that can consistently convert 2D to 3D data with high accuracy, especially in scenarios involving dynamic facial expressions, diverse facial angles, and complex environmental conditions. Therefore, an approach using the triangulation method for 3D face reconstruction in the real world was developed. In the experiments, two cameras were used to obtain two face landmark coordinates so that the triangulation method can be implemented for 3D face reconstruction. This research aims to develop a motion capture approach that is able to accurately and efficiently transform 2D data into 3D face models without the need for complex hardware. The main contribution of this research is the development of a machine learning-based markerless motion capture technique designed to improve the accuracy of face position prediction in 3D face reconstruction from 2D data in realistic environments. This method seeks to bridge the current technology gap by providing a more flexible and reliable solution, expanding the potential applications of motion capture in various fields without dependence on specialized hardware. The results of face reconstruction research using markerless motion capture and triangulation method show RMSE values of 3.560839 for eyes, 1.644749 for nose, and 4.054638 for lips.]]>
Copyrights © 2025
