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:许多研究人员从最近的传播概率模型中观察到了丰硕的成果,这些天正在积极研究其图像生成的有效性。在本文件中,我们的目标是评估3D人类运动相关任务的扩散概率模型的潜力。为此,本文件介绍了利用传播概率模型对先前观察到的运动的未来3D人类运动进行预测的研究。根据人类3.6M和HumanEva-I数据集,我们的结果显示,在完成一个单一的培训过程之后,扩散概率模型对于单项(确定性)和多项(随机)3D运动预测任务都是竞争性的。此外,我们发现,扩散概率模型可以提供一种有吸引力的妥协,因为它们可以在预测的未来运动的可能性和多样性之间达成正确的平衡。我们的代码可以在项目网站上公开查阅:https://sites.google.com/view/difnation-motion-pretrition。</s>