Identity Refinement without Re-Editing: Training-Free Enhancement of Identity Fidelity

Person-centric image editing aims to edit an image while preserving the target identity. However, existing identity-preserving methods mainly inject identity information during editing, where identity competes with other editing objectives and can leave residual identity mismatches. Correcting residual identity errors is typically handled by re-editing the whole image, which provides no explicit mechanism for correcting the identity mismatch and may further disturb already satisfactory content. This paper proposes a training-free post-hoc identity refinement framework that improves identity fidelity directly from an edited candidate without re-editing the image from scratch. At each refinement step, the flow state is decomposed into a clean-image endpoint and a complementary noisy-side endpoint. Identity and preservation objectives are applied to the decoded clean endpoint, while the complementary endpoint is retained during exact flow recomposition. To avoid mis-scaling identity guidance for small faces, we introduce support-aware gradient calibration, which estimates gradient magnitude over the facial support. Moreover, a Core–Halo–Background preservation strategy uses region-dependent candidate-trajectory anchoring to protect facial boundaries while retaining already satisfactory non-facial content. The proposed framework is training-free and can be applied across different flow-based models, while achieving SOTA identity refinement and outperforming both training-free and trained identity-preserving baselines. On the MultiID-Bench dataset, the proposed method improves ArcFace, FaceNet, and AdaFace similarity by 16.0%, 12.1%, and 8.1%, respectively, while retaining 99.97% non-target identity and 98.0% outside-target SSIM, achieving the best performance.