[2603.00607] IdGlow: Dynamic Identity Modulation for Multi-Subject Generation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00607 (cs) [Submitted on 28 Feb 2026] Title:IdGlow: Dynamic Identity Modulation for Multi-Subject Generation Authors:Honghao Cai, Xiangyuan Wang, Yunhao Bai, Tianze Zhou, Sijie Xu, Yuyang Hao, Zezhou Cui, Yuyuan Yang, Wei Zhu, Yibo Chen, Xu Tang, Yao Hu, Zhen Li View a PDF of the paper titled IdGlow: Dynamic Identity Modulation for Multi-Subject Generation, by Honghao Cai and 12 other authors View PDF HTML (experimental) Abstract:Multi-subject image generation requires seamlessly harmonizing multiple reference identities within a coherent scene. However, existing methods relying on rigid spatial masks or localized attention often struggle with the "stability-plasticity dilemma," particularly failing in tasks that require complex structural deformations, such as identity-preserving age transformation. To address this, we present IdGlow, a mask-free, progressive two-stage framework built upon Flow Matching diffusion models. In the supervised fine-tuning (SFT) stage, we introduce task-adaptive timestep scheduling aligned with diffusion generative dynamics: a linear decay schedule that progressively relaxes constraints for natural group composition, and a temporal gating mechanism that concentrates identity injection within a critical semantic window, successfully preserving adult facial semantics without overriding child-like anatomical structures. To resolve attribute leakage and semantic ambiguity without...