标题： SMooGPT：使用大型语言模型的风格化运动生成 显示英文标题

标题： SMooGPT: Stylized Motion Generation using Large Language Models

摘要： 风格化运动生成在计算机图形学中得到了积极研究，尤其是在扩散模型快速发展的推动下。这项任务的目标是生成一种新的运动，既尊重运动内容又符合所需的运动风格，例如“像猴子一样绕圈行走”。现有的研究尝试通过运动风格迁移或条件运动生成来解决这个问题。它们通常将运动风格嵌入到潜在空间中，并在潜在空间中隐式地引导运动。尽管取得了进展，但它们的方法存在可解释性差和控制能力有限、对新风格的泛化能力受限，以及由于公共风格化数据集中的强烈偏差，无法生成除“行走”之外的运动。在本文中，我们提出从推理-组合-生成的新视角来解决风格化运动生成问题，基于我们的观察：i）人类运动通常可以使用以身体部位为中心的自然语言有效描述，ii）大型语言模型（LLMs）表现出强大的理解和推理人类运动的能力，iii）人类运动具有固有的组合性质，使得通过有效的重新组合来生成新的运动内容或风格成为可能。因此，我们提出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LLM，在生成所需的风格化运动时充当推理者、组合者和生成者。我们的方法在身体部位文本空间中执行，具有更高的可解释性，能够实现细粒度的运动控制，有效解决运动内容和风格之间的潜在冲突，并由于LLMs的开放词汇能力而很好地泛化到新风格。全面的实验和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。 显示英文摘要

摘要： 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.