We address the problem of generating diverse 3D human motions from textual
descriptions. This challenging task requires joint modeling of both modalities:
understanding and extracting useful human-centric information from the text,
and then generating plausible and realistic sequences of human poses. In
contrast to most previous work which focuses on generating a single,
deterministic, motion from a textual description, we design a variational
approach that can produce multiple diverse human motions. We propose TEMOS, a
text-conditioned generative model leveraging variational autoencoder (VAE)
training with human motion data, in combination with a text encoder that
produces distribution parameters compatible with the VAE latent space. We show
the TEMOS framework can produce both skeleton-based animations as in prior
work, as well more expressive SMPL body motions. We evaluate our approach on
the KIT Motion-Language benchmark and, despite being relatively
straightforward, demonstrate significant improvements over the state of the
art. Code and models are available on our webpage.

本文介绍了使用文本描述生成多样的 3D 人类动作的方法，并提出了 TEMOS 框架，它是一种基于变分自编码器的文本条件生成模型，可以产生多种不同的人体动作，实验证明 TEMOS 框架在 KIT Motion-Language 基准测试中取得了显著的改进。