用自然语言统一控制服装生成的姿势与光照，实现更真实个性化的虚拟试衣。

Realistic and controllable garment synthesis is essential for fashion e-commerce, yet it demands precise coordination between human pose geometry and environmental photometry. Conventional pose-guided frameworks suffer from two fundamental limitations: they rely heavily on predefined skeletons from off-the-shelf estimators, restricting semantic flexibility; and they predominantly focus on studio-like generation under neutral lighting, failing to reconcile geometric configurations with complex, scene-specific illumination described in natural language. To bridge this gap, we propose FashionPose, a cascaded architecture that reconciles geometric and photometric control within a unified language-driven interface. Unlike conventional frameworks, our framework employs a decoupled yet synergistic strategy: (1) a bidirectional contrastive alignment mechanism that grounds textual semantics into an explicit geometric manifold, enabling template-free pose generation; (2) an identity-anchored synthesis module that translates these geometric priors into high-fidelity imagery while preserving fine-grained appearance; and (3) a prompt-conditioned relighting module that leverages the generated pose as a spatial anchor to achieve environment-aware shading. This hierarchical design effectively transforms high-level instructions into consistent visual representations, ensuring both structural precision and atmospheric harmony. To facilitate this paradigm, we construct PoseCap, a dataset with over 40,000 caption-keypoint pairs. Extensive experiments demonstrate that FashionPose outperforms existing benchmarks in pose accuracy and physical realism, providing a robust solution for personalized, scene-aware virtual fashion displays.