MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware
  Alignment and Disentanglement
Authors: name: Dong She, name: Siming Fu, name: Mushui Liu, name: Qiaoqiao Jin, name: Hualiang Wang, name: Mu Liu, name: Jidong Jiang

Multi-subject personalized generation presents unique challenges in
maintaining identity fidelity and semantic coherence when synthesizing images
conditioned on multiple reference subjects. Existing methods often suffer from
identity blending and attribute leakage due to inadequate modeling of how
different subjects should interact within shared representation spaces. We
present MOSAIC, a representation-centric framework that rethinks multi-subject
generation through explicit semantic correspondence and orthogonal feature
disentanglement. Our key insight is that multi-subject generation requires
precise semantic alignment at the representation level - knowing exactly which
regions in the generated image should attend to which parts of each reference.
To enable this, we introduce SemAlign-MS, a meticulously annotated dataset
providing fine-grained semantic correspondences between multiple reference
subjects and target images, previously unavailable in this domain. Building on
this foundation, we propose the semantic correspondence attention loss to
enforce precise point-to-point semantic alignment, ensuring high consistency
from each reference to its designated regions. Furthermore, we develop the
multi-reference disentanglement loss to push different subjects into orthogonal
attention subspaces, preventing feature interference while preserving
individual identity characteristics. Extensive experiments demonstrate that
MOSAIC achieves state-of-the-art performance on multiple benchmarks. Notably,
while existing methods typically degrade beyond 3 subjects, MOSAIC maintains
high fidelity with 4+ reference subjects, opening new possibilities for complex
multi-subject synthesis applications.