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.

翻译：随着众多研究者观察到近期扩散概率模型的丰硕成果，其在图像生成领域的有效性正受到广泛研究。本文旨在评估扩散概率模型在3D人体运动相关任务中的潜力。为此，我们开展了一项研究，探索利用扩散概率模型根据先前观测到的运动来预测未来3D人体运动。基于Human 3.6M和HumanEva-I数据集的研究结果表明，该模型在完成单次训练后，在单一（确定性）与多重（随机性）3D运动预测任务中均展现出竞争力。此外，我们发现扩散概率模型能够提供颇具吸引力的折中方案——其在预测未来运动的似然性与多样性之间取得了理想平衡。我们的代码已在项目网站公开：https://sites.google.com/view/diffusion-motion-prediction。