Forensic facial reconstruction currently relies on subjective manual methods to reconstruct a recognizable face from a skull. Automated approaches using algorithms and statistical norms have been able to reliably construct faces in real-time, but are unable to generate areas of the face that do not correlate strongly to the bone beneath, such as the eyes, lips and ears. Recent developments in deep learning have shown that generative models can produce realistic images indistinguishable from genuine human faces. Applying these techniques, we propose a generative deep learning solution to perform facial reconstruction directly from the bone with limited data and no background expertise. The model is trained on 665 3D Computed Tomography (CT) head scans that have been cleaned of noise, rotated to correct orientation and then filtered by density to find bone and soft tissue to be used as input and label respectively. It is trained with a combination of adversarial and VGGFace2 perceptual loss. The model is then compared to two baseline deep generative models and achieves a mIoU of 0.9410 and facial detection score of 88.32%. Results show the model is able to consistently generate accurate jaw and muscle structures. Additionally, it generates realistic ears, eyes and noses - features that are traditionally difficult to generate automatically with traditional techniques. Our model provides the basis for a complete, end-to-end solution for forensic facial reconstruction, with no prior knowledge or training on reconstruction techniques.