You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: 3D model. 3D face reconstruction is also a way to enrich the diversity of training data. They utilize 3D structure information to model the transformation between poses. 3D models first use 3D face data to obtain morphable displacement fields and then apply them to obtain 2D face data in different pose angles. There is a large number of papers about this domain, but we only focus on the 3D face reconstruction using deep methods or used for deep FR. In [47], Masi et al. generated face images with new intra-class facial appearance variations, including pose, shape and expression, and then trained a 19-layer VGGNet with both real and augmented data. Masi et al. [48] used generic 3D faces and rendered fixed views to reduce much of the computational effort. Richardson et al. [49] employed an iterative 3D CNN by using a secondary input channel to represent the previous network’s output as an image for reconstructing a 3D face as shown in Fig. 13. Dou et al. [51] used a multi-task CNN to divide 3D face reconstruction into neutral 3D reconstruction and expressive 3D reconstruction. Tran et al. [53] directly regressed 3D morphable face model (3DMM) [155] parameters from an input photo by a very deep CNN architecture. An et al. [156] synthesized face images with various poses and expressions using the 3DMM method, then reduced the gap between synthesized data and real data with the help of MMD.

Question: How has the quality and diversity of generated 3D face images improved over time, and what advances have contributed to these improvements?
The paper only talks about the line of work on 3D image reconstruction, in other words, the methods and approaches to reconstruct 3D face images. However, the quality and improvements that were made are not mentioned explicitly, but only referenced.