I am looking for the right AI tool to interpolate between binary images to create an animation. The images show segmented cells of a certain plant structure. The input would be start and end point images of the growth/evolution of the structure and the output would be a series of possible transition images.
These are meant for illustration purposes only, there is no criterion for truth as the transitions are unknown and these won't even be treated as hypotheses. Ideally, "realism" would be achieved by the AI seeing individual blobs in the image as cells and re-arranging/dividing/re-shaping/eliminating them accordingly to achieve the transitions.
For example, the transition between the two images below would have to add cells and make more lobes in some plausible way.
What would be the right neural network(s) to try on this? Is there something that might be able to do it out of the box or would one have to try train it in some way? It seems like if we can make stable diffusion animations from only the text prompts for the first & last images, this task is much simpler, but may remain unexplored because it's so specific.