Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization

Pose-ICL is a novel framework that introduces 3D-aware In-Context Learning (ICL) for subject customization in image generation, enabling effective pose control through multiple paired image-pose references. It employs Surface-Anchored Position Embedding (SAPE) to enhance 3D awareness by anchoring image tokens to volumetric bounding box coordinates, ensuring compatibility with existing DiT models. This approach significantly improves pose accuracy and identity consistency over current methods, addressing critical limitations in 2D-native architectures for practitioners focused on advanced image generation techniques.