Real world use cases of Multi-Party Computation Most of the research papers give imaginary applications of multi-party computation.

Either they talk about Yao's millionares' problem or two or more corporates willing to compute some intrustion detection stuff collectively on their private data without revealing their network logs or similar.

In reality, what are the practical use cases for multi-party computation?

Sharemind is trying to achieve cloud privacy of data computations (of single party) by distributing to multiple cloud servers but here there are no multiple parties as such.

While Homomorphic encryption is touted as special case of SMC, but its still single party outsourcing computations.
As I mentioned in a comment, a relatively new application of multi-party computation is its use as a countermeasure against (mainly hardware) side-channel attacks. In particular, there is a method called "threshold implementations" which is based on a form of secret sharing.

A relevant reference would be the paper Secure Hardware Implementations of Nonlinear Functions in the Presence of Glitches by S. Nikova, V. Rijmen and M. Schläffer.
Extensions to higher-order DPA attacks have also been proposed.

Let's say you wish to securely implement a Boolean function $f$ with $n$ input bits. The (secret) input $x = (x_1, \ldots, x_n)$ is then "shared" by (randomly) choosing additive shares $x_{i, j}$ such that

$$x_i = \bigoplus_{j = 1}^{s_{in}}x_{i, j}.$$

I'll write $\mathbf{x} = (x_{1, 1}, x_{1, 2}, \ldots, x_{1, s_{in}}, x_{2, 1}, \ldots, x_{n, s_{in}})$ for convenience below.

The idea is then to share $f$ too, in such a way that for all inputs $x$ and correct sharings $\mathbf{x}$

$$f(x) = \bigoplus_{i = 1}^{s_{out}} f_i (\mathbf{x}).$$

This is called the correctness property.
We now also require that $f_i$ is independent of every $i$th share of $x$. So each $f_i$ actually operates on $n(s_{in} - 1)$ rather than $ns_{in}$ inputs. This is called the non-completeness property.

Under the non-completeness assumption, the paper shows that if $x$ is shared with independent uniformly chosen shares, then $f_i$ (as well as any intermediate result) is independent of the input shares $x_{k, i}$ with $k = 1, \ldots, n$.
This then leads to pipelined implementations which are secure against first-order (i.e. using first-order statistical moments) side-channel attacks. For more details, take a look at the paper (or other papers on the same subject).