Gesture recognition with Hidden Markov Models
I have used HMM for gesture recognition (not pose recognition). What I did was: tracking the hand and recognize the gesture the hand was drawing in the air, you can image it as a trail.

You can use HMM as sequence recognizer, so first of all you need to transform your image into a discrete number sequence.

For each gesture you want to recognize, you need to train an HMM for that. 

So you have a dictionary with some known word. Each one is a trained HMM. If you have a new word (unknown obdervation) you can compute the probability for each word of the dictionary to be likely the unknown one.

Some pseudo-code:

##################### training phase

examples = [112233, 11233, 123, 1122223333]
word1 = train(example)

examples = [222333, 22222223333, 2222333333]
word2 = train(example)

examples = [124555, 1122445, 1111111222224444555]
word3 = train(example)

dictionary = [word1, word2, word3]


##################### recognition phase

#let's say you have a new unkown word: 12245, you want to know what word of the dictionary it is more likly

unkown = 12245
probabilities = []
for w in word:
    probabilities.append( calculate_prob(unkown) )


Now, see what the is the maximum value in probabilities and you get the most likely word of the dictionary!

See here:

http://www.ece.ucsb.edu/Faculty/Rabiner/ece259/Reprints/tutorial%20on%20hmm%20and%20applications.pdf

http://www.codeproject.com/Articles/69647/Hidden-Markov-Models-in-C

http://www.creativedistraction.com/demos/gesture-recognition-kinect-with-hidden-markov-models-hmms/