  We propose a simple and novel method for generating 3D human motion from
complex natural language sentences, which describe different velocity,
direction and composition of all kinds of actions. Different from existing
methods that use classical generative architecture, we apply the Denoising
Diffusion Probabilistic Model to this task, synthesizing diverse motion results
under the guidance of texts. The diffusion model converts white noise into
structured 3D motion by a Markov process with a series of denoising steps and
is efficiently trained by optimizing a variational lower bound. To achieve the
goal of text-conditioned image synthesis, we use the classifier-free guidance
strategy to fuse text embedding into the model during training. Our experiments
demonstrate that our model achieves competitive results on HumanML3D test set
quantitatively and can generate more visually natural and diverse examples. We
also show with experiments that our model is capable of zero-shot generation of
motions for unseen text guidance.
