Another popular method is "trivariate reduction" which samples $X_1 \sim Y+Z$ and $X_2 \sim W+Z$ so that the correlation is induced by the random variate $Z$. Note that this is also generalizable to more than 2 dimensions-but is more complicated than the 2-d case. You might think you can only get positive correlations but in fact you can also get negative correlations by using $U$ and $(1-U)$ when generating random variates, this will induce a negative correlation on the distributions.
A third popular method is (NORTA) NORmal To Anything; generate correlated normal variates, make them into uniform random variates via evaluating their respective cdfs, then use these "new" uniform random variates as a source of randomness in generating draws from the new distribution.
Besides the copula (a whole class of methods) approach mentioned in another post, you can also sample from the maximal coupling distribution which is similar in spirit to the copula approach. You specify marginal distributions and the sample from the maximal coupling. This is accomplished by 2 accept-reject steps as described by Pierre Jacob here. Presumably this method can be extended to higher dimensions than 2 but might be more complicated to achieve. Note that the maximal coupling will induce a correlation that depends on the values of the parameters of the marginals see this post for a nice example of this in Xi'an's answer to my question.
If you are willing to accept approximate (in most cases) samples then MCMC techniques are also an option to sample from multi-dimensional distributions.
Also, you could use accept-reject methods but it is typically hard to find a dominating density to sample from and evaluate the ratio of that to the desired density.
This is all the additional methods I can think of but there are probably a couple I missed.