MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware
  Alignment and Disentanglement
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.
68b8f158d43cadaf7a688afb
Dong She
68b8f158d43cadaf7a688afc
Siming Fu
admin_assigned
2025-09-04T19:41:37.052Z
6485dd6d07a2c1915060f603
/avatars/8594d647359a7d19ab29b8ec91d1444e.svg
fu
user
simingfu
68b8f158d43cadaf7a688afd
Mushui Liu
68b8f158d43cadaf7a688afe
Qiaoqiao Jin
admin_assigned
2025-09-04T19:41:42.767Z
656066e3d3f1b94f3b923239
/avatars/445c187d39b45ff5570cbd743817c193.svg
Qiaoqiao Jin
user
Au233
68b8f158d43cadaf7a688aff
Hualiang Wang
admin_assigned
2025-09-04T19:41:54.752Z
651cdb3dfafb0777f212c5ef
/avatars/dc319a87eff9e609360ef2fce51e755d.svg
Wang Hualiang
user
HualiangWang
68b8f158d43cadaf7a688b00
Mu Liu
68b8f158d43cadaf7a688b01
Jidong Jiang
MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware
  Alignment and Disentanglement
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.
MOSAIC framework enhances multi-subject image generation by ensuring precise semantic alignment and orthogonal feature disentanglement, achieving high fidelity even with multiple references.
representation-centric framework semantic correspondence orthogonal feature disentanglement SemAlign-MS semantic correspondence attention loss multi-reference disentanglement loss