Human motion modeling is important for many modern graphics applications,
which typically require professional skills. In order to remove the skill
barriers for laymen, recent motion generation methods can directly generate
human motions conditioned on natural languages. 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. Instead of
a deterministic language-motion mapping, MotionDiffuse generates motions
through a series of denoising steps in which variations are injected. 2)
Realistic Synthesis. MotionDiffuse excels at modeling complicated data
distribution and generating vivid motion sequences. 3) Multi-Level
Manipulation. MotionDiffuse responds to fine-grained instructions on body
parts, and arbitrary-length motion synthesis with time-varied text prompts. Our
experiments show MotionDiffuse outperforms existing SoTA methods by convincing
margins on text-driven motion generation and action-conditioned motion
generation. A qualitative analysis further demonstrates MotionDiffuse's
controllability for comprehensive motion generation. Homepage:
this https URL

MotionDiffuse 是一种基于扩散模型的文本驱动运动生成框架，可以通过概率映射、真实的合成方式，以及多级操作等方法，有效地生成复杂和多种变化的人类运动。

运动漫化：基于扩散模型的文本驱动人体动作生成

MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model

To represent people in mixed reality applications for collaboration and
communication, we need to generate realistic and faithful avatar poses.
However, the signal streams that can be applied for this task from head-mounted
devices (HMDs) are typically limited to head pose and hand pose estimates.
While these signals are valuable, they are an incomplete representation of the
human body, making it challenging to generate a faithful full-body avatar. We
address this challenge by developing a flow-based generative model of the 3D
human body from sparse observations, wherein we learn not only a conditional
distribution of 3D human pose, but also a probabilistic mapping from
observations to the latent space from which we can generate a plausible pose
along with uncertainty estimates for the joints. We show that our approach is
not only a strong predictive model, but can also act as an efficient pose prior
in different optimization settings where a good initial latent code plays a
major role.

针对在协作和交流中需要产生逼真可信的虚拟人物角色，本文提出了一种基于流的 3D 人体生成模型，通过学习 3D 人体姿势的条件分布和从观察到的数据到潜在空间的概率映射，生成能够确定关节不确定度的虚拟角色动作，证明了该方法不仅是一个强大的预测模型，还可以在各种优化环境中充当一个高效的姿势先验。