让文字生成动作时考虑人的年龄性别等属性，更真实。
Generating Attribute-Aware Human Motions from Textual Prompt
- 用因果模型分离动作语义与人体属性，实现精准控制
- 构建首个带属性标注的文本-动作数据集，支持评估
- 适合需要个性化动作生成的研究与应用
文本驱动的人体动作生成近年来备受关注，可基于文本描述生成动作。然而现有方法忽视了年龄、性别、体重和身高等人体属性对动作模式的关键影响。本文首次探索填补这一空白，将每个动作视为动作语义与人体属性的组合，其中文本仅对应动作语义。为此，提出一种受结构因果模型启发的新框架，实现动作语义与人体属性的解耦，支持文本到语义的预测及属性可控生成。所提模型能根据用户输入的文本与属性生成符合要求的动作。为评估，构建包含属性标注的文本-动作对综合数据集，确立首个属性感知动作生成基准。大量实验验证了模型的有效性。
原文摘要 · Abstract (English)
Text-driven human motion generation has recently attracted considerable attention, allowing models to generate human motions based on textual descriptions. However, current methods neglect the influence of human attributes-such as age, gender, weight, and height-which are key factors shaping human motion patterns. This work represents a pilot exploration for bridging this gap. We conceptualize each motion as comprising both attribute information and action semantics, where textual descriptions align exclusively with action semantics. To achieve this, a new framework inspired by Structural Causal Models is proposed to decouple action semantics from human attributes, enabling text-to-semantics prediction and attribute-controlled generation. The resulting model is capable of generating attribute-aware motion aligned with the user's text and attribute inputs. For evaluation, we introduce a comprehensive dataset containing attribute annotations for text-motion pairs, setting the first benchmark for attribute-aware motion generation. Extensive experiments validate our model's effectiveness.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。