  We present a novel generalized zero-shot algorithm to recognize perceived
emotions from gestures. Our task is to map gestures to novel emotion categories
not encountered in training. We introduce an adversarial, autoencoder-based
representation learning that correlates 3D motion-captured gesture sequence
with the vectorized representation of the natural-language perceived emotion
terms using word2vec embeddings. The language-semantic embedding provides a
representation of the emotion label space, and we leverage this underlying
distribution to map the gesture-sequences to the appropriate categorical
emotion labels. We train our method using a combination of gestures annotated
with known emotion terms and gestures not annotated with any emotions. We
evaluate our method on the MPI Emotional Body Expressions Database (EBEDB) and
obtain an accuracy of $58.43\%$. This improves the performance of current
state-of-the-art algorithms for generalized zero-shot learning by $25$--$27\%$
on the absolute.
