Although tremendous progress has been made in generative and transferable backmapping in recent years, future work will enable extensibility or fine-tuning to arbitrary CG maps, chemical systems, and thermodynamic state points; incorporate physical priors to provide informative inductive biases; enforce the Boltzmann distribution in the generated conformational ensemble; and improve inference efficiencies to enable applications to ultra-large molecular systems and on-the-fly backmapping of biomolecular simulations.