Using AI for human like mouse movement between two points I am trying to use Neural Networks or ML to make my mouse cursor move as humanly as possible, I want to be able to generate a set of points which are needed for the mouse to move from point A (Starting Point) to point B (Target Point). I have a dataset where I recorded the mouse movement from one point to another in the format of x,y,time.
I have attached an image to show the data. (The number of points that it takes from Point A to Point B varies for different cases, some cases it takes 100 points to get to target while in other cases it could take less or more).
Now I have a dataset that has multiple points recorded from A to B and in the final solution I aim to have a model file which takes input of the Starting Point and Ending Point (x,y) coordinates on the screen and have the model generate output of the points that are needed to get to the target point.
The dataset is something like this : 
I need suggestions in order to do this using Neural Networks or Machine Learning. How should I set my data for input and output in order to get the result I want?
 A: This isn't a proper answer, but if I had to solve this problem (and only had a few hours to do so) I would just find the trajectory in the training set that was most similar to the target in terms of distance and direction (or a random choice if there are several similar trajectories) and return a translated version of that.
A: This is a very nice example/application of cGAN, conditional generative adversarial network... you have a dataset made of mouse tracking, so you set as labels initial and final point, and you feed those 2 points to a generator (plus some gaussian noise), and all 3  informations to a discriminator.
When the discriminator won't be anymore able to classify the generator outputs, you will have a generator that takes in input start and end fo the mouse track, and outputs a sequence of intermediate points, which you can interpolate though
NOTE:
you will probably have to look for recurrent cGAN, as you generator outputs a sequence, and the discriminator takes in input a sequence
