Why RMSProp converges faster than Momentum? Why is RMSProp in many cases converging faster than Momentum?

Momentum:

$$v_{dW} := \beta v_{dw} +(1-\beta)dW$$
$$W := W-\alpha v_{dw}$$

RMSProp:

$$ S_{dw} := B \cdot S_{dw} + (1-B)\cdot (dW)^2$$
  $$W := W- \alpha \frac{dW}{\sqrt{S_{dw}}}$$

Where $\alpha$ is the learning rate (0.01 etc), $\beta$ is the momentum term (0.9 etc), similar to B

From my point of view, both momentum and RMSProp have "tendency to keep moving". Well, I can see how RMSprop will naturally accelerate on flat surfaces due to

$$\frac{1}{\sqrt{S_{dw}}}$$

when $S_{dw}$ is small, but is there another benefit that RMSprop provides?
The basic intuition is that you should not have the same learning rate for different dimensions. For instance, you can have a high slope in one direction but not for another. Consequently, you should not have the same speed for the two directions. Momentum adds acceleration. Suppose gradient is your instant velocity and the average is your average velocity. Momentum is actually viscosity or somehow friction. Suppose that you are near your optimal points, your gradients become zero and you have low average which means your speed changes slowly. They have both alpha term but what is going to be used is the running average, just a kind of average which is simple to be calculated. Take a look at here and here for making an analogy.