3d human motion generation from the text via gesture action classification and the autoregressive model

TL;DR

This paper introduces a deep learning framework that generates realistic 3D human motions from text descriptions by combining gesture classification with an autoregressive model, enhancing naturalness and variability.

Contribution

It presents a novel approach integrating text classification and autoregressive motion generation, including new loss functions, data augmentation, and stop tokens for variable-length motions.

Findings

Successfully generates perceptually natural 3D motions from text

Achieves good cross-dataset generalization performance

Proposes effective data augmentation and motion embedding techniques

Abstract

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.