It's not as "huge", as they make it look. The goal of the technique is to increase data efficiency (number of real world tries it needs to learn). So instead of using real trajectories it simulates trajectories (that's planning), and learn from these.

These lines of ideas is not new. The main problems associated with it are that it is almost always more computationally expensive (you learn from real and dreamed trajectories) and it is harder to learn as it is susceptible to a kind of exposure bias : Once you have built a model like "the earth is flat", then you will simulate/dream trajectory according to it, diluting the weak evidence you can get from real data telling you that the "earth is round", and so it gets stuck with a wrong model.

The performance gain you refer to is a gain relative to a naive way of doing things i.e. working in pixel space.

Don't get me wrong, I'm a big fan of the model based approach, and every small step in this direction is good as it helps with explain-ability. This paper is one of these nice small steps, but doesn't compare to the gain of previous techniques like experience-replay, or hindsight-experience-replay.