pose-icl-3d-aware-in-context-learning-for-pose-controllable-subject-customization-acd70fee·1 events·first seen Aliases: Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization
Researchers introduce Pose-ICL, a tuning-free framework for generating images of user-specified subjects with accurate pose control. The method uses Surface-Anchored Position Embedding (SAPE) to give 2D diffusion models explicit 3D awareness by anchoring image tokens to volumetric bounding box surface coordinates. Evaluations on 3D assets and real-world subjects show improvements over existing methods in both pose accuracy and identity consistency. The framework is designed for compatibility with existing Diffusion Transformer (DiT) models.