The official PyTorch implementation of the paper "Human Motion Diffusion Model" Resources

2022年9月29日 · In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for the human motion domain. MDM is transformer-based, combining insights from motion generation literature.

2023年4月29日 · 这篇文章提出了 Motion Diffusion Model (MDM) ，一个应用于人体动作领域的 classifier-free diffusion-based 的生成式模型。M

现在有一些方法成功了（Motion Clip & TEMOS），证明了text到motion的 映射 是可以被合理学习的；不过这些方法学习的分布是有限的，因为使用的是AE/VAE，它们的encoder和decoder学习到的只能是 高斯分布 ——VAE用高斯分布生成图像而diffusion使用了高斯分布的噪声进行 ...

2023年6月15日 · 本文主要根据的是文章《Human Motion Diffusion as a Generative Prior 》中的内容，链接和代码都在主页： Diffusion想要真的在图像生成上有比较好的效果，所需的资源往往是普通消费者乃至普通研究者承担不起的。

In this paper, we introduce Motion Diffusion Model (MDM), a carefully adapted classifier-free diffusion-based generative model for the human motion domain. MDM is transformer-based, combining insights from motion generation literature. A notable design-choice is the prediction of the sample, rather than the noise, in each diffusion step.

2022年12月5日 · To address this issue, we present a novel physics-guided motion diffusion model (PhysDiff), which incorporates physical constraints into the diffusion process. Specifically, we propose a physics-based motion projection module that uses motion imitation in a physics simulator to project the denoised motion of a diffusion step to a physically ...

2022年8月31日 · However, it remains challenging to achieve diverse and fine-grained motion generation with various text inputs. To address this problem, we propose MotionDiffuse, the first diffusion model-based text-driven motion generation framework, which demonstrates several desired properties over existing methods. 1) Probabilistic Mapping.

Our PhysDiff model generates physically-plausible motions using a physics-based motion projection in the diffusion process, eliminating artifacts such as floating, ground penetration, and foot sliding, often observed with state-of-the-art models.

2024年9月22日 · MDM（Human Motion Diffusion Model） 是一个基于扩散模型的人体动作生成框架，由 Guy Tevet 等人开发。该模型通过深度学习技术，能够从文本描述或动作指令中生成逼真的人体动作序列。