  After many researchers observed fruitfulness from the recent diffusion
probabilistic model, its effectiveness in image generation is actively studied
these days. In this paper, our objective is to evaluate the potential of
diffusion probabilistic models for 3D human motion-related tasks. To this end,
this paper presents a study of employing diffusion probabilistic models to
predict future 3D human motion(s) from the previously observed motion. Based on
the Human 3.6M and HumanEva-I datasets, our results show that diffusion
probabilistic models are competitive for both single (deterministic) and
multiple (stochastic) 3D motion prediction tasks, after finishing a single
training process. In addition, we find out that diffusion probabilistic models
can offer an attractive compromise, since they can strike the right balance
between the likelihood and diversity of the predicted future motions. Our code
is publicly available on the project website:
https://sites.google.com/view/diffusion-motion-prediction.
