Is it possible to train a DL model that will generate a full resolution 2D image based on few numbers describing this image and what type of model or architecture would that be?
What I want to achieve is that I deliver to the model some numbers for example describing positions of objects on the screen and number describing how lit the scene is and I get back a 2D image with objects in their correct positions and proper lighting, but for one set of input data values I will get always one same image (see image above). These input data also could be anything else than positions and lighting, these are only examples helping to visualize what I mean.
This all, of course, assuming that I have a lot of annotated training data that consists of images and labels of the objects' positions and scene lighting values.
EDIT: The final model would be trained on real images taken from Full HD camera, not some simple shapes like presented here, that I did only to explain better my question.