Recently, significant progress has been made in text-based motion generation,
enabling the generation of diverse and high-quality human motions that conform
to textual descriptions. However, it remains challenging to generate
fine-grained or stylized motions due to the lack of datasets annotated with
detailed textual descriptions. By adopting a divide-and-conquer strategy, we
propose a new framework named Fine-Grained Human Motion Diffusion Model
(FG-MDM) for human motion generation. Specifically, we first parse previous
vague textual annotation into fine-grained description of different body parts
by leveraging a large language model (GPT-3.5). We then use these fine-grained
descriptions to guide a transformer-based diffusion model. FG-MDM can generate
fine-grained and stylized motions even outside of the distribution of the
training data. Our experimental results demonstrate the superiority of FG-MDM
over previous methods, especially the strong generalization capability. We will
release our fine-grained textual annotations for HumanML3D and KIT.

通过细粒度人体动作扩散模型 (FG-MDM) ，我们成功地生成了符合文本描述的多样化和高质量的人类动作，即使在训练数据分布之外。

使用 ChatGPT 改进的描述生成精细人类动作

Generating Fine-Grained Human Motions Using ChatGPT-Refined Descriptions

Denoising diffusion models have shown great promise in human motion synthesis
conditioned on natural language descriptions. However, it remains a challenge
to integrate spatial constraints, such as pre-defined motion trajectories and
obstacles, which is essential for bridging the gap between isolated human
motion and its surrounding environment. To address this issue, we propose
Guided Motion Diffusion (GMD), a method that incorporates spatial constraints
into the motion generation process. Specifically, we propose an effective
feature projection scheme that largely enhances the coherency between spatial
information and local poses. Together with a new imputation formulation, the
generated motion can reliably conform to spatial constraints such as global
motion trajectories. Furthermore, given sparse spatial constraints (e.g. sparse
keyframes), we introduce a new dense guidance approach that utilizes the
denoiser of diffusion models to turn a sparse signal into denser signals,
effectively guiding the generation motion to the given constraints. The
extensive experiments justify the development of GMD, which achieves a
significant improvement over state-of-the-art methods in text-based motion
generation while being able to control the synthesized motions with spatial
constraints.

本文提出了应用于人体动作合成的有向扩散模型的算法 GMD，并进行了大量实验验证，证明该算法在控制生成的动作的同时，显著提高了基于文本的运动生成的效果。