Synthetic data generation using data from 2 different distributions

Lets say we have the following:

  • data A : N x data points, features: f1, from distribution d1
  • data B : M x data points, features: f2, from distribution d2
  • data C : M x data points, features: f3, from distribution d3


f1 = f2 + f3

Is there a way to compile data B and C into a new data D with f1 features such that it looks like it is coming from distribution d1?

I can explain it by an example:

A shows a dog and a cat playing together in a garden, while B and C show a dog and a cat playing alone in the garden. Can we generate the simultaneous play behavior from their solo play behaviors and implicit features learned from small dataset A?