Techniques are described for object insertion via scene graph. In implementations, given an input image and a region of the image where a new object is to be inserted, the input image is converted to an intermediate scene graph space. In the intermediate scene graph space, graph convolutional networks are leveraged to expand the scene graph by predicting the identity and relationships of a new object to be inserted, taking into account existing objects in the input image. The expanded scene graph and the input image are then processed by an image generator to insert a predicted visual object into the input image to produce an output image.