  We present Text2Gestures, a transformer-based learning method to
interactively generate emotive full-body gestures for virtual agents aligned
with natural language text inputs. Our method generates emotionally expressive
gestures by utilizing the relevant biomechanical features for body expressions,
also known as affective features. We also consider the intended task
corresponding to the text and the target virtual agents' intended gender and
handedness in our generation pipeline. We train and evaluate our network on the
MPI Emotional Body Expressions Database and observe that our network produces
state-of-the-art performance in generating gestures for virtual agents aligned
with the text for narration or conversation. Our network can generate these
gestures at interactive rates on a commodity GPU. We conduct a web-based user
study and observe that around 91% of participants indicated our generated
gestures to be at least plausible on a five-point Likert Scale. The emotions
perceived by the participants from the gestures are also strongly positively
correlated with the corresponding intended emotions, with a minimum Pearson
coefficient of 0.77 in the valence dimension.
