用大模型直接通过自然语言操控3D人体形状，实现快速虚拟形象定制。
BodyShapeGPT: SMPL Body Shape Manipulation with LLMs
- 用微调的大模型理解人体描述并生成SMPL-X参数
- 可基于文本指令精确控制3D人体体型特征
- 适合虚拟人设计、游戏建模等需要快速生成的场景
生成式AI为复杂任务提供高效工具。其中大型语言模型（LLMs）在生成多样文本方面表现突出。本文探索微调后的LLMs识别人物物理描述的能力，并通过推断SMPL-X模型的形状参数，创建准确的虚拟角色。我们证明，经过训练的LLMs能够理解并操作SMPL的形状空间，从而实现通过自然语言控制3D人体形态。该方法有望提升人机交互体验，并为虚拟环境中的个性化与仿真开辟新途径。
原文摘要 · Abstract (English)
Generative AI models provide a wide range of tools capable of performing complex tasks in a fraction of the time it would take a human. Among these, Large Language Models (LLMs) stand out for their ability to generate diverse texts, from literary narratives to specialized responses in different fields of knowledge. This paper explores the use of fine-tuned LLMs to identify physical descriptions of people, and subsequently create accurate representations of avatars using the SMPL-X model by inferring shape parameters. We demonstrate that LLMs can be trained to understand and manipulate the shape space of SMPL, allowing the control of 3D human shapes through natural language. This approach promises to improve human-machine interaction and opens new avenues for customization and simulation in virtual environments.
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