3D human modeling has been widely used for engaging interaction in gaming, film, and animation. The customization of these characters is crucial for creativity and scalability, which highlights the importance of controllability. In this work, we introduce Text-guided 3D Human Generation (\texttt{T3H}), where a model is to generate a 3D human, guided by the fashion description. There are two goals: 1) the 3D human should render articulately, and 2) its outfit is controlled by the given text. To address this \texttt{T3H} task, we propose Compositional Cross-modal Human (CCH). CCH adopts cross-modal attention to fuse compositional human rendering with the extracted fashion semantics. Each human body part perceives relevant textual guidance as its visual patterns. We incorporate the human prior and semantic discrimination to enhance 3D geometry transformation and fine-grained consistency, enabling it to learn from 2D collections for data efficiency. We conduct evaluations on DeepFashion and SHHQ with diverse fashion attributes covering the shape, fabric, and color of upper and lower clothing. Extensive experiments demonstrate that CCH achieves superior results for \texttt{T3H} with high efficiency.

翻译：三维人体建模已广泛应用于游戏、电影和动画中的交互场景。角色的定制化对创造力和可扩展性至关重要，凸显了可控性的重要性。本文提出文本引导的三维人体生成（Text-guided 3D Human Generation，\texttt{T3H}）任务，要求模型根据服装描述生成三维人体。该任务具有两个目标：1）生成的三维人体应具备精细的关节运动能力；2）其着装需受给定文本控制。为解决\texttt{T3H}任务，我们提出组合式跨模态人体（Compositional Cross-modal Human，CCH）模型。CCH采用跨模态注意力机制，将组合式人体渲染与提取的服装语义相融合，使各人体部位感知相应的文本引导视觉模式。我们融入人体先验与语义判别机制，增强三维几何变换与细粒度一致性，从而支持模型仅从二维数据集中高效学习。在DeepFashion和SHHQ数据集上的评估覆盖了上下装形状、面料及颜色等多样化服装属性。大量实验表明，CCH能以高效率实现\texttt{T3H}任务的优异结果。