Abstract
Stylized motion generation is actively studied in computer graphics, especially benefiting from the rapid advances in diffusion models. The goal of this task is to produce a novel motion respecting both the motion content and the desired motion style, e.g., ``walking in a loop like a Monkey''. Existing research attempts to address this problem via motion style transfer or conditional motion generation. They typically embed the motion style into a latent space and guide the motion implicitly in a latent space as well. Despite the progress, their methods suffer from low interpretability and control, limited generalization to new styles, and fail to produce motions other than ``walking'' due to the strong bias in the public stylization dataset. In this paper, we propose to solve the stylized motion generation problem from a new perspective of reasoning-composition-generation, based on our observations: i) human motion can often be effectively described using natural language in a body-part centric manner, ii) LLMs exhibit a strong ability to understand and reason about human motion, and iii) human motion has an inherently compositional nature, facilitating the new motion content or style generation via effective recomposing. We thus propose utilizing body-part text space as an intermediate representation, and present SMooGPT, a fine-tuned LLM, acting as a reasoner, composer, and generator when generating the desired stylized motion. Our method executes in the body-part text space with much higher interpretability, enabling fine-grained motion control, effectively resolving potential conflicts between motion content and style, and generalizes well to new styles thanks to the open-vocabulary ability of LLMs. Comprehensive experiments and evaluations, and a user perceptual study, demonstrate the effectiveness of our approach, especially under the pure text-driven stylized motion generation.
Abstract (translated)
在计算机图形学领域，风格化运动生成是一个活跃的研究方向，尤其是得益于扩散模型的快速进步。该任务的目标是产生一种新颖的运动模式，同时尊重原有的动作内容和所需的动作风格，例如“像猴子一样循环行走”。现有研究试图通过动作样式转换或条件动作生成来解决这个问题。它们通常将动作风格嵌入到潜在空间中，并在相同的潜在空间中隐式引导动作。尽管取得了进展，但这些方法仍然存在解释性差、控制力弱以及无法泛化至新样式的限制，由于公共样例数据集中强烈的偏差问题，导致除了“行走”之外的动作难以生成。 在这篇论文中，我们提出了从推理-组合-生成的新视角来解决风格化运动生成的问题。基于我们的观察：i) 人类动作通常可以通过身体部位为中心的自然语言有效描述；ii) 大型语言模型（LLMs）在理解和推断人类动作方面表现出强大的能力；iii) 人类动作具有内在的组合特性，有助于通过有效的重组来生成新的动作内容或风格。因此，我们建议利用身体部位文本空间作为中间表示，并提出SMooGPT——一个经过微调的大型语言模型，在生成所需的样式化运动时充当推理者、组合器和生成器的角色。我们的方法在人体部位文本空间中执行，具有更高的解释性，支持精细的动作控制，有效解决动作内容与风格之间的潜在冲突，并且由于大型语言模型的开放词汇能力而很好地泛化到新样式。 综合实验、评估以及用户感知研究证明了我们方法的有效性，特别是在纯文本驱动的风格化运动生成方面的优势。
URL
https://arxiv.org/abs/2509.04058
