  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.
