The efficiency of convolutional neural networks (CNNs) facilitates 3D face reconstruction, which takes a single image as an input and demonstrates significant performance in generating a detailed face geometry. The dependence of the extensive scale of labelled data works as a key to making CNN-based techniques significantly successful. However, no such datasets are publicly available that provide an across-the-board quantity of face images with correspondingly explained 3D face geometry. State-of-the-art learning-based 3D face reconstruction methods synthesize the training data by using a coarse morphable model of a face having non-photo-realistic synthesized face images. In this article, by using a learning-based inverse face rendering, we propose a novel data-generation technique by rendering a large number of face images that are photo-realistic and possess distinct properties. Based on the real-time fine-scale textured 3D face reconstruction comprising decently constructed datasets, we can train two cascaded CNNs in a coarse-to-fine manner. The networks are trained for actual detailed 3D face reconstruction from a single image. Experimental results demonstrate that the reconstruction of 3D face shapes with geometry details from only one input image can efficiently be performed by our method. Furthermore, the results demonstrate the efficiency of our technique to pose, expression and lighting dynamics.