NVIDIA has published research demonstrating the use of machine learning to infer a 3D model from a single 2D image. From the blog:

In traditional computer graphics, a pipeline renders a 3D model to a 2D screen. But there’s information to be gained from doing the opposite — a model that could infer a 3D object from a 2D image would be able to perform better object tracking, for example.

NVIDIA researchers wanted to build an architecture that could do this while integrating seamlessly with machine learning techniques. The result, DIB-R, produces high-fidelity rendering by using an encoder-decoder architecture, a type of neural network that transforms input into a feature map or vector that is used to predict specific information such as shape, color, texture and lighting of an image.

The paper, "Learning to Predict 3D Objects with an Interpolation-Based Renderer" is available here.