Subject Customization is a foundational task in modern image generation. By providing a few reference images and a text prompt, users can generate images of a specific object in any desired scene. However, existing methods still struggle to achieve effective pose control for customized subjects. In practice, they often exhibit inaccurate poses or inconsistent cross-pose appearances. These limitations suggest that understanding objects in a volumetric manner remains a significant challenge for 2D-native backbones. To address this challenge, we propose Pose-ICL, a tuning-free framework that leverages 3D-aware In-Context Learning (ICL) to directly adapt to new subjects through multiple paired image-pose references. Its core mechanism,Surface-Anchored Position Embedding (SAPE), equips the model with explicit 3D awareness by anchoring image tokens to the surface coordinates of a volumetric bounding box. Dedicated refinements ensure its seamless compatibility with existing DiT models. Extensive evaluations on both 3D assets and real-world subjects demonstrate that Pose-ICL significantly outperforms current methods in both pose accuracy and identity consistency.

翻译：主体定制是现代图像生成中的基础任务。通过提供少量参考图像和文本提示，用户可在任意期望场景中生成特定对象的图像。然而，现有方法在实现针对定制主体的有效姿态控制方面仍存在困难。在实践中，它们常表现出不准确的姿态或不一致的跨视角外观。这些局限性表明，以体积方式理解对象仍然是二维原生主干架构的重大挑战。为应对这一挑战，我们提出Pose-ICL，一种免调优框架，通过利用三维感知上下文学习（ICL）直接适配新主体，借助多组图像-姿态配对参考。其核心机制——表面锚定位置嵌入（SAPE）——通过将图像标记锚定到体积边界框的表面坐标，为模型赋予显式三维感知能力。专用优化确保了其与现有DiT模型的无缝兼容性。对三维资产和真实世界主体的广泛评估表明，Pose-ICL在姿态准确度和身份一致性方面均显著优于现有方法。