  In this paper, a deep learning-based model for 3D human motion generation
from the text is proposed via gesture action classification and an
autoregressive model. The model focuses on generating special gestures that
express human thinking, such as waving and nodding. To achieve the goal, the
proposed method predicts expression from the sentences using a text
classification model based on a pretrained language model and generates
gestures using the gate recurrent unit-based autoregressive model. Especially,
we proposed the loss for the embedding space for restoring raw motions and
generating intermediate motions well. Moreover, the novel data augmentation
method and stop token are proposed to generate variable length motions. To
evaluate the text classification model and 3D human motion generation model, a
gesture action classification dataset and action-based gesture dataset are
collected. With several experiments, the proposed method successfully generates
perceptually natural and realistic 3D human motion from the text. Moreover, we
verified the effectiveness of the proposed method using a public-available
action recognition dataset to evaluate cross-dataset generalization
performance.
