Methods and apparatuses for generating an image generative model are disclosed. An embodiment comprises: acquiring a sample set, a sample comprising a first depth image, a second depth image and a visible image; and executing training based on the sample set: inputting the second depth image and the visible image of at least one sample in the sample set respectively into a pre-established initial neural network model to obtain a generated depth image corresponding to each of the at least one sample; calculating a similarity between the generated depth image corresponding to the each of the at least one sample and a corresponding first depth image; determining whether the initial neural network model reaches a preset optimization goal based on the calculation result; and using the initial neural network model as the trained image generative model, in response to determining the initial neural network model reaching the preset optimization goal.