An Online Tool for the Annotation of 3D Models
Connah Kendrick, Kevin Tan, Tomos G. Williams, Moi Hoon Yap · 2017
Annotation of data is fundamental for training any modern facial tracking system. Methods, such as deep and machine learning, require large amounts of pre-annotated data to produce results found in many state-of-the-art systems. 2- Dimensional (images) annotations rely on the texture information of the face to annotate the features. However, in 3-Dimensions (models), the task becomes more complex. In 3D, the conventional 2D approaches are ineffective as facial landmarks are difficult to accurately identify without texture information. There has been little research into the accuracy of methods for annotating 3D facial models. This paper proposes a method for annotating 3D models which uses texture information by aligning a model to a 2D image to compare its accuracy and throughput to the conventional methods of 3D annotation. For evaluation, 16 nonexpert volunteers were recruited and instructed to annotate three models using both the proposed method and the conventional method. The resultant annotations were compared to ground truth data generated by an experienced annotator. The results demonstrate significant improvement in the throughput of the proposed annotation method compared to the conventional approach without significant differences in accuracy. The proposed method also highlights that the conventional method does not successfully identify all the facial landmarks. The proposed method will be made freely available to use online.