MOSAIC: Multi-Subject Personalized Generation via Correspondence-Aware Alignment and Disentanglement
Authors: Dong She
Siming Fu
Mushui Liu
Qiaoqiao Jin
Hualiang Wang
Mu Liu
Jidong Jiang
Venue: ICLR 2026 Poster (2026)
Keywords: Multi-Subject Personalized Generation
Diffusion Model
Primary area: generative models
TL;DR: MOSAIC

Abstract: 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 SOTA 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.

Published as a conference paper at ICLR 2026
MOSAIC: MULTI-SUBJECT PERSONALIZED GENER- ATION VIA CORRESPONDENCE-AWARE ALIGNMENT AND DISENTANGLEMENT
Dong ; Siming Fu-xt, Mushui Liu"*, Qiaoqiao Jin Hualiang Wang
Mu Liu', Jidong Jiang
'ByteDance "Zhejiang University Thee Hong Kong University of Science and Technology {shedong, fusiming, jingiaoqiao, liumu, jiangjidong]Bbyteeee. com, ims0zju. edu. edu hwangfd
ABSTRACT
Multi-subjecct personalized generation presents unique challenges in maintaining identity fidelity and semantic coherence when synthesizing images conditioned multiple reference subjects. Existing methods often suffer from identity blend- ing and attribute leakage due to inadequate modeling of how different subjects should interact within shared representation spaces. We present MOSAIC, representation-centre 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 im- age should attend to which parts of each reference. To enable this, we introduce SemAlign-MS, meticulously annotated dataset providing fine-grained semantic correspondences between multiple reference subjects and target images, previ- ously 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 its designated re- gions. Furthermore, we develop the multi-reference disentanglement loss to push different subjects into orthogonal attention subspaces, preventing feature interfer- ence while preserving individual identity characteristics. Extensive experiments demonstrate that MOSAIC achieves SOTA 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.
INTRODUCTION
Multi-subject personalized generation in controllable image synthesis faces significant challenges in maintaining identity consistency while preventing attribute entanglement. Recent approaches have explored various strategies to address this problem: MS-Diffusion (Wang et al. 2025) and SSR-Encoder (Zhang al.] 2024c) incorporate spatial layout guidance into cross-attention layers to bind subjects to dedicated regions, while DreamO (Mou et al. 2025b) embeds routing con- straints directly into DiT blocks for architectural-leell control. TFCustom (Liu al., 2025) takes frequency domain approach, introducing time-aware guidance to inject low- and gigh-frequency ref- erence features at appropriate denoising stages. Most recently, XVerse (Chen al. 2025) introduces
token-specific modulation offsets for independent subject representation control.
However, these methods share a critical limitation: they lack explicit optimization of the underlying diffusion representations for both precise multi-subject alignment with target images and effective disentanglement between reference subjects. This shortcoming becomes increasingly severe as sub- ject count grows, with most methods experiencing significant degradation beyond 3-4 subjects due
Equull contribution. 'Project leader. Cooresponding author. Source code available at: httpp///ggtttb...cc////yeeeeeeee fanqie ii/MOAA

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Published as a conference paper at ICLR 2026
MOSdIC
MOSAIC
MUSAIC
somonn a holcing 1D weeden board
a 1 logn
man und a noman ane stencing on ofther
lige
A man 4 momonn and Boy an befind hoblc with
CocaCola
CLUSTON DESIGN
ains noxt tax arii sently h
can, cSp, a to and lay cur ore
The for fortoon choractari etoed toy ther. creating livly atroophere
headphores ands Trenir shords bealkk a nomon wearing uop and dross
Figure I: MOSAIC achieves high-quality personalized generation across single-subject and multi- subject scenarios. It demonstrates superior subject consistency in complex compositional settings.
to compounding feature interference. To understand why existing approaches fail, we analyze the fundamental requirements for multi-subject generation and identify two key deficiencies. The first concerns the correspondence problem: without explicit modeling of which specific reference image regions should attend to target latent parts, models cannot maintain semantic coherence across mul- tiple subjects. The second involves multi-reference feature integration: when multiple subjects share the same latent space, their representations interfere, yet existing methods provide no mechanism to explicitly disentangle these conflated features. These observations lead eritical question: how can we design optimization objectives that simultaneously preserve individual subject fidelity while enforcing inter-subject separability between different subjects' representations?
To address these fundamental limitations, we propose MOSAIC, a principled framework that re- representation optimization problem. The
formulates multi-subject personalized generation foundation of MOSAIC is the establishment of explicit semantic correspondences through a care- fully curated dataset featuring densely annotated alignment points across reference-target image pairs. This correspondence foundation enables direct supervision of attention mechanisms for both raference-target alignment and inter-subject differentiation critical capability absent in existing approaches. Leveraging this semantic point correspondence, MOSAIC implements two comple- mentary optimization objectives. The alignment objective employs cross-entropy supervision over attention distributions conditioned on correspondence labels, enforcing precise spatial mappings be- tween reference and target representations. This mechanism ensures semantically coherent feature propagation from reference images to their designated target locations. Simultaneously, the disen- tanglement objective maximizes inter-subject attention divergence via symmetric KL regularization, promoting orthogonal attention patterns across reference subjects. This formulation effectively mit- igates cross-subject feature interference-a persistent challenge in multi-reference scenarios. No- tably, our method enables high-quality consistent generation with 4+ reference subjects, capability that current approaches cannot achieve. Our contributions are as follows:
We propose MOSAIC, a representation-cennri framework that learns subject-consistent, disentangled representations by explicitly supervising attention correspondence between target and reference images, combining alignment and disentanglement objectives.
We introduce SemAlign-MS, the first large-scale, annotated dataset with fine tie-ggraned se- mantic correspondences specifically tailored for multi-subject driven generation.
2

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P.rrrpp Generation
Imagge Synthesis
Extraction
Generation
5.Smanttic Correspondence
Siyle-Suiject
5eg
map
stands
animal
exply platform
beside aestld
nutooss
Kontext
object
feet and vicln as they queet
Figure 2: SemAlign-MS Dataset Construction Pipeline. Five-stage systematic pipeline for gener- ating high-quality multi-reference training data with validated semantic correspondences.
We demonstrate significant improvements over existing methods, particularly in challeng- ing scenarios with 4+ subjects, while maintaining computational efficiency through our plug-and and-play design.
RELATED WORKS
Subject-Driven Image Generation. Subject-driven image generation aims to synthesize images that preserve the identity and appearance of reference subjects while following textual descriptions. Recent advances have focused on improving reference encoding and attention mechanisms within diffusion frameworks. OminiControl (Tan et al., 2025) exploits the generative model itself reference image encoder, demonstrating the capability of diffusion transformers in maintaining sub- ject consistency. For multi-subject scenarios, UNO (Wu al. 2025b) proposes systematic data generation pipeline, while DreamO (Mou et al.
2025a) constructs a router mechanism to focus at- 2025) adopts text-stream modulation transform
tention on target subjects. XVerse (Chen al.
reference images into token-specific offsets, enabling better integration of subject-specific cues. However, existing methods primarily rely on global feature matching. lacking explicit fine ane-grained detail constraints between reference and target regions. This limitation often results in imprecise spatial alignment, reduced reference fidelity, and attribute entanglement when handling multiple subjects simultaneously. Our work addresses these fundamental challenges through explicit seman- tie point correspondences that enable precise spatial alignment and cross-ree reference disentanglement, substantially enhancing subject consistency while preventing inter-subject interference.
Visual correspondence establishes spatial relationships between semantically similar regions across images, serving foundation for various computer vision tasks (Lee et al. 2019 2020 Lu et al. (024). Traditional methods rely on handcrafted fea- tures like SIFT (Lowe 2004) and SURF (Bay al.) (006), while recent deep learning approaches
Visual Correspondence for Generation.
leverage supervised learning with annotated datasets (Ham et al., 2017 Min et al. 2019 Yu et al.) 2021 promising direction has emerged using diffusion models for correspondence estimation. Methods such as DIFT (Tang et al. 2.23), SD-DINO (Zhang et al. (023). ), and Geo SSC (Zhang al. (024a) demonstrate that trained diffusion features can establish reliable correspondences across diverse images without extensive supervision. However, these correspondences have not been effectively utilized for multi-subject generation tasks. Our work bridges this gap by being the first to leverage semantic point correspondences for multi-suuject-drrien generation. We establish sys- tematic pipeline that constructs high-quality correspondences and explicitly incorporates them into the generation process through our semantic corresponding attention alignment and multi-reference disentanglement mechanisms.
3 SEMALIGN-MS! A HIGH-QUALITY MULTI-SUBJECT DATASET WITH SEMANTIC POINT CORRESPONDENCES
Our data construction follows a systematic five-stage pipeline designed to ensure both diversity and quality. We first leverage GPT-4o (Hurst et al. 2024) with carefully designed templates to automati-

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Published as a conference paper at ICLR 2026
dog,
LaCA
slondng
Text
Target
Ref1
Ref2
RefK
Ref Torget Attention
Lable
T5
VAE
Noise
RoPE
Align
MM Atteniioioon
LoRA
LO
FFN
LORA
VAE
Aftenton Score SorrDribuha
Aftentin Score Distnipttion
Disentangle
of
Figure 3: Overview of MOSAIC Framework. enables high-fidelity multi-subject generation through two complementary mechanisms. (Blue region) The Semantic Correspondence Atten- tion Loss (SCAL) enforces precise point-to-point alignment between reference tokens and their corresponding locations in the target latent, ensuring high consistency. (Green region) The Multi- Reference Disentanglement Loss (MDL) maximizes the divergence between different references' attention distributions, pushing each subject into orthogonal representational subspaces.
cally generate diverse prompts containing multi-subject, encompassing various combinations of peo- plc, animals, objects, and their interactions to ensure comprehensive coverage of multi-subject sce- narios commonly encountered in real-world applications. The generated prompts are then processed through state-of-the-art T2I models to synthesize images, where we implement multi-criteria au- tomated filtering strategy that evaluates image quality, subject clarity, and compositional coherence to retain only the highest-quality synthetic images. Subsequently, we employ Lang-SAM (Kirillov) et al., 2023) for robust open-vocabulay detection and segmentation of all subjects within the syn- thesized images, enabling precise identification and isolation of individual subjects regardless of their semantic category and providing the foundation for subsequent correspondence establishment. Finally, utilize FLUX Kontext (Labs et al. 2025) for viewpoint correction while maintaining semantic consistency, significantly enhancing dataset diversity and ensuring comprehensive appear- coverage across different viewpoints and poses.
Once we obtain the large-scale data, we establish semantic point correspondences between each target image and multiple reference images, where each reference image contributes p(k) sampled semantic points from the reference space that mapped corresponding locations in the target latent space, and p(k) E N is the number of semantic correspondence points for the k-th reference image. Formally, let
((II(
[(0)) ) denote our dataset, where N is the total number 1 : I(0)
of target images in the training batch, K the maximum number of reference images, I(O repre- sents the i-th target image, and I(i/) denotes the k-th reference image for the i-th target. When fewer than K reference images are available, we pad the remaining slots with black placeholder images maintain consistent batch dimensions, For each pair (i,)) ,I((i) ). we define the semantic correspondence set as:
((..k) {(uig) v1g) p(A)
where ú., represents the j-th sampled point coordinate reference image I and v.j denotes its corresponding semantic point in the target latent space. To prevent inter-reference conflicts where references map to the same target token, we enforce correspondence disjointness across different reference images:
(((A)) {Viss (us,jr Us,)) C(1.k)). s.t. (((k)) n ((ikk)) = 0. Vki + k2,
(2)

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Published as a conference paper at ICLR 2026
where ((iA) represents the token position corresponding the j-th sampled token of k-th refer- ence image for the i-th training sample. This constraint ensures that each target latent location Vi. is associated with at most reference image, preventing ambiguous supervision where multiple references compete for the same target region. Through this systematic pipeline, we successfully collect 1. 1.2M high-quality image pairs with validated semantic correspondences that form the foun- dation of SemAlign-Mo dataset. Additional data pipeline details are provided in the Appendix.
4 METHODOLOGY
4.1 OVERVIEW OF MOSAIC
R==1 depicting different subjects, a text prompt T, and target image Iu to be generated. The VAE encoder then transforms both the target image and all reference images into latent representations:
Architecture. shown in Fig. 3 given K reference images {I (T(K)
Zut VAE (Ige)
(kc) Ziet
VAE
(3)
where k = I, K. During training, noise is applied to the target image latent to obtain Anoise igi Zigt + e, where e ~ (0, 1). The text prompt is encoded using T5 (Raffel al. 2023 to obtain text embeddings C TT).). To ensure spatial disentanglement between reference and target latents, modified Rotary Position Embeddings (RoPE) (Su et al. 2024) with distinct frequency bases are applied.
Melti-Reeerence within MM-Attention. The core of MOSAIC lies in how multiple reference latent and target latent interact through attention. Following OmniControl (Tan et al. 2025). employ LoR aaRA-auumented branch for reference processing while maintaining the original model weights for the denoising branch. Crucially, concatenate all reference latents into unified representation to enable joint processing. In each transformer block I, the attention computation proceeds as:
iig (1)
fo(z )
(4)
Que
V (1)
fo(C)),
Text
text
where fo and fo mean the mm-attention projection weights for target latent and text embedding. For the reference images, we first concatenate their latent representations:
Zref
(1)
Zee 2re
(1.1).
(2.1).
i (K.))
(5)
Then process them through the LoRA branch of fe. which denoted as A
A0 (eee))
ref
ref
These features are then concatenated across modalities and processed through multi-head attention:
Q(1)
Q
(1)
(1)
(4)
Agi
Mexts
(())
(1)
(())
gg
texts
ref
V (V())
( lexti
(
igi
ref
The attention scores are computed as (1) softmax Q (KUYT// /Vd), where d is the feature dimension. Note that within A (1), the reference-to-targe latent attention sub-matrix A Aff E N captures how each token from all reference images attends to the noisy latent, where Nee is the total number of tokens length across all reference images and Ngg is the token length of the target latent.
4.2 SEMANTIC CORRESPONDING ATTENTION ALIGNMENT
To faithfully preserve fine- tne-grained semantic details. especially in regions requiring precise structural correspondence, we design the semantic correspondence attention loss (SCAL) to explicitly enforce point-wise semantic alignment within the siffrence-to-targee attention mechanism.
5

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Published as a conference paper at ICLR 2026
As shown in Fig. the reference-to-tautee target latent attention allows each token from the reference images to attend to all token positions of the noised target latent. For a reference token at position and target latent position the average reference to target latent attention across all selected DiT blocks Natock is computed as:
Niteck
exp (Q.K. /Va)
A ter (1,
Nblock
Ees exp
Nug
2u.K/ /vd)
where Qu and K, are the query and key embeddings for the reference token at position and latent position v, respectively. To map local reference coordinates to global token indices concatenated reference representation, we define:
G(
(idx)
(R) +ugs
(9)
ida=1
offsct
(k) (k)
where idæ denotes the reference subject index. For each correspondence pair (Mi. 0i5 ) super- vise the attention from the reference token position u that focused its corresponding latent
position Vig define:
(k)
K
p(k)
LSCA
Σ
P(k) Σ log A (g(
(0) Vif
(10)
K
R=
By integrating LScA into the training objective, our model is effectively encouraged to learn precise semantic mappings between reference and generated images. This leads to significantly improved preservation of local structure, textures, and fine details, going beyond the limitations of global similarity or implicit feature alignment.
4.3 MULTI-REFERENCE CORRESPONDING DISENTANGLEMENT
While alignment ensures high consistency, a crucial aspect of multi-subject driven generation the potential interference among different reference images. We introduce multi-reference disentangle- ment loss (MDL), which promotes distinct attention patterns across references. MDL emphasizes the differentiation of attention maps from various subjects, thus preventing feature conflation.
Specifically, for k-th reference image, we collect the attention patterns at correspondence locations. For each correspondence point (uj, Uy) E C(K.k);
aj [A agi [u,, 1) It E Negt] E RN
(11)
Subsequently, we aggregate these attention responses for each reference:
pik)
(k)
a
(k)
E ER
(12)
p(b
where || denotes the normalization operation. Then the distance between (+)) and a is:
dist a ( a (()))) = Dxi. (a (a) + Dxi. (a 6) (a())
(13)
where DRL is KL. divergence. Subsequently, we enforce distinct attention patterns across references:
LMD K(K -
Σ Σ
dist(a ( (())))
(14)
j=1jil
By maximizing attention pattern divergence, this loss prevents references from competing for the same attention regions, mitigating cross-reference feature interference. The overall loss is defined as:
L
Laitt + aLSSA + BLMD
(15)
where Las is the mot-mat loss used (Esser et al. 2024), a and / are two factors that balance the weight

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison for single-subject and multi--ubbject on DreamBench benchmark.
Reference
Method
CLIP-1 1
CLIP-T
DINO 1
UnifiedReward
HPSv3
DreamBooth
80.30
30.52
66.81
3.30
8,40
BLIf-Diffusion
80.47
30.24
69.82
3.05
8.96
Single Single-Subjen Subjectt
SSR-Encoder
82.10
30.79
61.22
3.27
9.25
MS- NS-Diffusion
80.82
31.05
70.32
3.70
9,40
UNO
83,50
30.41
75.97
4,00
11.24
DreamO
83.35
30.61
76.03
4.33
12.78
Veeesse
83.20
30.20
75.44
4,20
11.02
MOSAIC
84,30
31.64
77.40
4.40
14.36
MS-Diffusion
72.60
31.91
52.50
3.67
8.25
Multi-Sabjee
UNO
73.29
32.23
54.22
4.23
11.55
DreamO
73.32
32.10
52.17
4.33
13.25
XVerse
73.47
31.20
53.71
3.87
11.10
MOSAIC
76.30
32.40
56.83
4.39
14.90
Table 2: Quantitative results of single-subject and multi-subject driven generation VerseBench.
Single-Subject
Multi-Subject
Method
Overall
DPG
ID-Sim
IP-Sim
AES
ID-Sim
IP-Sim
AVG
MS-Diffusion
96.89
6.52
55.71
59.63
54.69
87.21
3.77
46.21
55.91
48.28
$1.49
UNO
89.65
47.91
80.40
55.90
68.47
85,28
31.82
67.00
54.24
59.59
64.03
DreamO
96.93
75.48
70.84
54.57
74.46
88.80
50.24
64.63
52.47
64.04
69.25
OmniGin22
92.60
62.41
74.08
52.34
70.36
91.55
40.81
67.15
51.40
62.73
66.55
X Verse
76.72
66.59
71.48
70.08
73.40
MOSAIC
96.55
81.98
80.92
60.77
88.94
69.90
74.27
72.03
76.04
5
EXPERIMENTS
5.1
EXPERIMENTAL DETAILS
base model. The rank of additional LoRA (Hu et al.] (022) is set as 128. During training, we employ the AdamW (Kingma & Ba 2015) optimizer with learning rate of Ie-4 and train the model for 100K steps, using a batch size of per GPU. The and /3 in Eq. 15 are set to 0.4 and 0.6.
Implementation Details In this work, adopt FLUX-1.0-DEEV (Labs 2024
Evaluation Setting
We evaluate our proposed method on DreamBench (Ruiz al. 2023 and XVrrseBench (Chen et al., 2025), focusing on both single-subject and multi-subject generation sce- narios. For comparison, we include DreamBooth (Ruiz et al. (003)),
(Li et al, SSR-Encoder ęZhang al. (022b)) MS-Diffusion (Wang et al. 2025). UNO (Wu et al.
2023
20256), DreamO (Mou et al.) 2025a), OmniGen2 (Wu et al. 2025a). and XVerse (Chen et al. DreamBench utilizes the CLIP-I (Radford al.) (021), DINO (Oquab et al. (023), and
2025).
CLIP-T (Radford et al. 2021) to evaluate the semantic and text-image alignment. XeereBeench
utilizes DPG score (Hu et al. 2024), ID-Sim (Deng al. 2019). IP-Sim (Oquab al. 2023
AFS [discus(4434 2024) to further evaluate the alignment and image quality.
5.2
MAIN RESULTS
Quantitative Results The results on DreamBench and XVerseBench are shown in Tab and respectively.
T
On DreamBench, MOSAIC achieves consistently strong results across all metrics. In the single- subject setting, it achieves 84.30 (CLIP-I), 31.64 (CLIP-T), and 77.40 (DINO), surpassing the second-best method UNO by 0.80 on CLIP-I, and the second-best method DreamO by 1.37 on DINO, indicating better image-text alignment and semantic fidelity. In the more challenging multi- subject setting, it maintains strong performance with 76.30 (CLIP-I). 32.40 (CLIP-T), and 56.83 (DINO), outperforming second-best competitors by approximately 3.0, 0.2, and 2.6 points, respec- tively. Furthermore, MOSAIC demonstrates a significant advantage in VLM-basee metrics across both scenarios, which align more closely with human visual preferences. It achieves the highest

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Published as a conference paper at ICLR 2026
Reference
Prompt
MOSAIC(Ours) MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
is ground.
101
Semnese stnetched letter henday and click.
to take picture
foucss an park.
plays guttor
whie citting
with her hat and beslpeck resting quietly of
mon and a womon steed noom with e amomonnt on tubee
Figure 4: Qualitative comparison on single and multi-subject driven generation. DreamO does not support 3+ reference images; masked regions show where comparison is unavailable, with visible results generated from two randomly selected references.
Table 3: Quantitative comparison with strong multi-subjectiddeniiy baselines. We report Identity Preservation (IP) and Text Consistency (PC) scores under zero-shot settings. MOSAIC significantly outperforms FastComposer and tace-Diffuser, particularly in multi-subject identity preservation.
Single-Subject IP f
Multi-Subject
Methods
PC f
IP
PC
FastComposer (zero-shot)
0.514
0.243
0,465
0.233
Subject-Diffusion (zero-shot)
0.605
0.228
0.435
0.210
Face- Face-Diffuser (zero-shot)
0.708
0.325
0.594
0.320
MOSAIC (zero-shot)
0.785
0.362
0.712
0.358
scores in UnifiedReward (4.40 for single-subject and 4.39 for multi-subject) and HPSv3 (14.36 and 14.90 ). substantially outperforming the saate-of-the-re method DreamO. These results strongly val- idate that MOSAIC not only excels in traditional metrics but also possesses superior visual aesthetic quality and comprehensive image-text consistency regardless of the subject complexity.
On XerseBennhh MOSAIC achieves the highest overall average XVerse's 's 73.40. And MOSAIC achieves a DPG score that is on par with the cop-performing DreamO (96.93). It also demonstrates clear advantages identity preservation, with ID-Sim scores of 81.98 for single-object and 69.90 for multi-object scenarios, as well as in perceptual similarity, with IP-Sim scores of 80.92 and 74.27, respectively. Overall, these demonstrate the robustness of proposed method under varying subject complexity.
of 76.04, surpassing
Comparison with Strong -rrntity-rreeeviig Methods To further validate MOSAIC's effec- tiveness against strong multi-subject and dlentity-preservinn methods, conducted supplementary comparisons following the official evaluation protocol of compared our method against FastComposer (Xiao at. 2023), Subject-Diffusion (Ma al. 2024
(Wang et al. (024). We
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Table 4: Impacts of different losses on DreamBench benchmark in multi-subject scenario.
LSCA
LMD
CLIP-I
CLIP-T
DINO
73.45
29.90
52.03
X
75.89
31.10
55.99
/
75.10
31.70
55.24
76.30
32.40
56.83
Reffrrince
w/oles
MOSATC
Prompt
fed
fd
MOSATC
solly beer and or sroggohhee oricpthhoss
OF the
Figure 5: (Left): Ablation study of MOSAIC. (Right): Visualization of attention maps from specific
reference regions to generated image regions. Zoom in for details.
and Face-Diffuser (Wang al. 2024) using Identity Preservation (IP) and Text Consistency (PC) metrics under both single-subject and multi-subjeet settings.
The results presented in Tab. MOSAIC exhibits significant advantages
these strong baselines. In the Single-Subject Scenario, our method achieves an IP score of 0.785, substan- tially outperforming Face-Diffuser (0.708) and FastComposer (0.514). The advantage is even more pronounced in the Multi-Subject Scenario, where MOSAIC achieves an IP score of 0.712, far sur- passing Diff-uiffsssr's s 0.594. We attribute this superior performance to our proposed fine- gra-gra alignment (LsCA) and spatial decoupling mechanism (CMD), which effectively resolve feature confu- sion between multiple subjects, thereby achieving state-of-the-e levels both identity preservation and text consistency.
Qualitative Results Fig. presents qualitative results across scenes with varying numbers ref- erence objects, demonstrating MOSAIC's superior performance in multi-subject generation. Ap- pearance consistency. Our method maintains strong visual coherence across different subjects. For instance, the snackers in rows 1-2 and the in row preserve consistent textures and shapes throughout the generated scenes, indicating effective feature transfer from reference images. Multi- subject handling. The advantages of our approach become particularly evident with three more reference subjects. In row 3, competing methods suffer from object omission and duplication: MS- Diffusion and OmniGen2 fail render the clock entirely, while XVerse produces duplicated and deformed cats. In contrast, MOSAIC accurately represents all three objects without such artifacts. Scalability to more complex scenes. For scenes involving four or more reference objects, exist- ing methods exhibit substantial degradation. As shown in row 6, only MOSAIC preserves facial consistency across multiple subjects, while other approaches show significant quality loss and iden- tity confusion. Overall, these results highlight MOSAIC's ability to generate high-quality, consis- tent outputs across diverse compositional scenarios, especially in challenging multi-subject settings where other methods fall short.
5.3 ABLATION STUDIES
Impact of Different Losses We conduct ablation studies to evaluate each component of our method on multi-subjects Dreambench, as shown Tab[4 Adding the semantic correspondence attention loss LSCA leads to consistent gains across all metrics, with CLIP-I improving from 73.45 to 75.89, CLIP-T from 29.90 to 31, and DINO from 52.03 to 55.99. This confirms that point-wise semantic supervision aids fine-grained detail retention. Conversely, adding LMD contributes more significantly to text alignment (CLIP-T), improving it by +1.80 (compared to +1.20 from LSCA)- Introducing LSCA and LMD further improves CLIP-I to 76.30, CLIP-T to 32.40, and DINO to 56.83, suggesting that suppressing cross-reference feature conflation strengthens semantic fidelity and compositional coherence. Moreover, visual results in Fig. Leett) improve progressively: the

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baseline yields blurry, inconsistent outputs, while adding LSca enhances composition and detail. The full model achieves both global scene coherence and local identity preservation by preventing semantic interference across references.
Visualization of attention maps Fig- Right) visualizes attention maps between reference fea- tures and target latents, revealing how our proposed losses improve spatial correspondence. We examine specific reference regions (e.g., teddy bear's nose, numerical digits on toys) and visualize their attention distributions across the target image, with attention weights averaged across trans- former layers. The baseline model shows dispersed and misaligned attention patterns, where ref- erence tokens often activate incorrect target regions, leading to feature mixing and loss of subject identity. Introducing LSCA guides reference tokens toward semantically appropriate target locations, yet the attention remains broadly distributed with notable overlap between different subjects (e.g., bear features bleeding into Minion regions). The addition of LMD significantly sharpens the at- tention focus-each reference token now precisely attends to its corresponding target region while suppressing responses to other subjects' areas. This progression (baseline + LSca + LScA + LMD) confirms hypothesis: alignment loss establishes semantic correspondence while disentanglement loss enforces spatial exclusivity, together enabling accurate multi-subject synthesis.
Mechanism Analysis: Step-by-Step Denoising. To intuitively understand how our proposed modules influence the generation process, we visualize the step-by-step denoising evolution (see Figure 7 in Appendix). Without our specific losses, the baseline model struggles with fine-grained identity preservation, often generating generic objects that lack specific design details found in the reference images. Introducing LSCA successfully anchors these identity features; however, without the disentanglement constraint (LMD), we observe severe feature entanglement. For instance, in multi-subject scenarios, visual attributes from one subject (e.g., the distinct yellow color of toy car) frequently bleed onto adjacent subjects (e.g., tinting a dog's 's fur with an unnatural yellowish hue). By further integrating LarD, this feature confusion is effectively suppressed. The model spatially decouples the attributes of different subjects, restoring their correct appearances while keeping dis- tinctive features strictly confined to their respective regions. This confirms that LScA is responsible for identity anchoring, while LMD is indispensable for enforcing spatial exclusivity and preventing cross-subject attribute leakage.
CONCLUSION
In this paper, we propose MOSAIC, representation-centii approach for multi-subject driven image generation. Our method features semantic correspondence pipeline, along with explicit attention alignment and disentanglement mechanisms, effectively addressing the key challenges of identity preservation and attribute entanglement in multi-subject personalized generation scenarios. We also curate and will release SemAlign-MS, a large-scale multi-subject dataset with fine-grained semantic point correspondences to facilitate future research in controllable generation.
Extensive experiments demonstrate that MOSAIC achieves state-of-the-art performance in both identity fidelity and semantic consistency across established benchmarks. A key strength of our method is its superior fidelity-diversity balance: unlike global constraints, our point-wise alignment targets only critical semantic features, preserving substantial freedom for non- essential attributes like pose and lighting. This enables both high generation diversity and identity preservation. Detailed analysis is provided in Appendix A.4. Furthermore, approach exhibits robust scalability, successfully handling complex compositions with 4+ reference subjects.
Linitations. While MOSAIC achieves strong multi-subject generation, it comes with increased computational cost during training. Future work will explore efficient multi-reference encod- ing strategies and attention compression techniques to reduce computational overhead while maintaining generation quality.
Use of LLMs. We utilize LLMs to assist with formula derivations and writing refinement.
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A ADDITIONAL QUALITATIVE EVALUATION AND MECHANISM ABLATION ANALYSIS
A.1 ABLATION ANALYSIS
A.1.1 IMPACT OF THE SEMALIGN-MS DATASET
Table 5: The impact of the SEMALIGN-MS dataset.
means trained ont the SEMALIGN-MS
dataset but has no SCAL and MDL.
Single-Subject
Multi-Subject
Method
Overall
DPG
ID-Sim
IP-Sim
AES
AVG
DPG
ID-Sim
IP-Sim
AES
AVG
MS-Diffusion
96.89
6.52
55.71
59.63
54,69
87.21
3.77
46.21
55.91
48.28
51.49
UNO
89.65
47.91
80.40
55,90
68.47
85.28
31.82
67.00
54.24
59.59
64.03
DreamO
96,93
75.48
70.84
54.57
74.46
50,24
64.63
52.47
64.04
69.25
OmniGen2
92.60
62.41
74.08
52.34
70.36
91.55
40.81
67.15
51.40
62.73
66.55
Verse
93.69
79.48
76.86
56.84
76.72
88,26
66.59
71.48
53.97
70.08
73.40
MOSAIC
91.24
75.11
75.62
56.37
74.59
85.46
60.09
68.55
53.39
66.87
70.73
MOSAIC
96.55
81.98
80.92
60.77
80.05
88.94
69.90
74.27
55.02
72.03
76.04
To further substantiate the effectiveness of the proposed SCAL and MDL modules, we performed ablation experiments to disentangle improvements introduced by the dataset itself. The results on XeerseBench are presented in Tab. 5 Here, MOSAIC* denotes the baseline trained the SEMALIGN-ME dataset without SCAL or MDL. As illustrated, integrating SCAL and MDL leads consistent gains across both single-reference and multi-reference settings. Specifically, ID-Sim improves by 6.87% and 9.81 % while IP-Sim increases by 5.30% and 5.72%, respectively. More- over, the overall score rises substantially from 70.73% to 76.04%. highlighting the effectiveness of our approach.
A.1.2 COMPONENT ANALYSIS OF SCAL AND MDL
To provide a deeper understanding of the individual contributions of our proposed modules, we con- ducted comprehensive ablation study on the DreamBench multi-refferece scenario. We evaluated the model performance under settings containing only LSCA, only L.D. and the full combination. The results summarized in Table
Table 6: Ablation study of SCAL and MDL modules on DreamBench multi-reference scenario. The results indicate that LSCA dominates visual identity preservation (CLIP-I, DINO), while LMD con- tributes significantly text alignment (CLIP-T) through spatial disentanglement. The full MOSAIC achieves the best performance via their synergy.
LSCA
LMD
CLIP-I
CLIP-T
DINO
X
73.45
29.90
52.03
X
75,89
31.10
55.99
X
75.10
31.70
55.24
V
76.30
32.40
56.83
Based on these results, make the following observations:
LSCA Dominates Visual Identity Preservation. Comparing the single-module results, I.SCA yields more significant improvement in visual metrics (CLIP-I +2.44, DINO +3.96) compared to LMD. This is because LscA enforces strict subject-level attention alignment between the reference and generated images. This strong visual constraint directly promotes the retention of fine-grained identity features.
LMD Prioritizes Text Alignment & Disentanglement. Conversely, LMD contributes more sig- nificantly to Text Alignment (CLIP-T), improving it by +1.80 (compared to +1.20 from LSCA). The core function of LaM is explicitly decouple multiple subjects within the prompt (e.g., distin-
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guishing "a dog" from "a cat"). Without this proper spatial decoupling, the model is prone subject confusion, which lowers prompt adherence.
Synergistic Effect. When combined, the model achieves the best performance across all metrics. This confirms that strong visual feature alignment (Lsca) and spatial multi-subject decoupling (LMD) complementary and essential for high-quality multi-subject generation.
A.1.3 GENERALIZATION TO EXTERNAL DATASETS
To demonstrate that MOSAIC is not limited to SemAlign-MS dataset and can generalize to other data sources, we conducted supplementary experiments on two widely used public datasets: Subject-200K Tan et al. (2025) SSigle-Subjecte and sul-subjectdrriven Xiao al. (2024) (Multi- Reference). We applied our automated pipeline to extract semantic points for these datasets without manual intervention.
Performance on Subject-200K. Since this dataset contains only single subjects, we enabled only LSCA- As shown in Table 7 method achieves significant improvements over the baseline DreamBench single-reference metrics.
Table 7: Generalization performance on Subject-200K dataset (Single-Subject Scenario).
LSCA
LMD
CLIP-I
CLIP-T
DINO
N/A
81.25
30.05
74.60
N/A
83.32
30.21
75.57
Performance on X2I-subject-driven. For this multi-subject dataset, we enabled both losses. The results in Table Cooffrmm that MOSAIC effectively generalizes to external multi-subject data, sig- nificantly boosting CLIP-I and DINO scores while maintaining high text alignment.
Table 8: Generalization performance on X2I-subject-driven dataset (Multi-Subjeet Scenario).
LSCA
LMD
CLIP-1
CLIP-T
DINO
71.30
29.65
51.73
x
75.35
31.08
55.49
V
74.92
31.60
55.06
V
75.70
32.15
56.20
These results validate that leveraging our high-aceuracy automated pipeline, MOSAIC can be easily transferred to other datasets to consistently enhance visual consistency and text control.
A.1.4 GENERALIZATION TO DIFFERENT BACKBONES (SDXL)
key concern raised regarding the fairness of comparison is whether MOSAIC's performance gains are solely due the powerful FLUX backbone. To demonstrate that our proposed modules (LsCA and LMD) are architecture-agnosii and generalize well to other backbones (e.g., from DiT to UNet), we applied MOSAIC to the widely used Stable Diffusion XL. (SDXL) model.
We compared our "MOSAIC-SDXL"" variant against mainstream SDXL-based personalization methods on the DreamBench benchmark. As shown Table MOSAIC-SDXS consistently out- performs existing baselines in both single-subject and multi-subject settings.
These results confirm that the effectiveness of SCAL and MDL dependent on a specific back- bone architecture but represents a robust contribution to the general personalization framework.
A.1.5 IMPACT OF SAMPLED NUMBER (kk
As shown in Fig. increasing ( from 50 200 consistently improves performance: rise- from 73.9 to 76.4, DINO from 53.8 to 57.0, and CLIP-T from 30.1 to 32.6. These gains indicate that denser semantic correspondences enhance attention supervision and fine-grained spatial alignment.
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Table 9: Generalization performance on SDXL backbone. We compare MOSAIC-SDXL against other SDXL-based methods on DreamBench. The results demonstrate that our method maintains SOTA performance even when applied to a UNet-based architecture, validating the robustness of our loss designs.
Reference
Method
CLIP-I f
CLIP-T
DINO
DreamBooth
80.30
30.52
66.81
BLIP-Diffusion
80.47
30.24
69.82
Single-Subject
SSR- 3SR-Encoder
82.10
30.79
61.22
MS- DS-DDifuusin
80.82
31.05
70.32
MOSAIC-SDXL (Ours)
82.15
31.20
72.45
MS- DSSDiifusson
72.60
31.91
52.50
Multi-Subject
MOSAIC-SDXL (Ours)
73.20
32.11
53.84
0.57
0.76
0.56
Velue
Value
0.75
0.55
0.14
0.310
0.54
033
CLIP-I
0.345
CLIP-T
0.5
DINO
0.72
0.500
0.52
159
Figure 6: Impact of different p(k) on DreamBench benchmark in multi-subject scenario.
However, performance gains diminish beyond certain point, with increased computational cost. We thus set p(k) 150 by default to balance accuracy and efficiency.
A.1.6 IMPACT OF APPLYING SCAL AND MDL TO DIFFERENT ATTENTION BLOCKS
Table 10: The impact of the number of attention blocks applied SCAL and MDL. e, m, and denote applying SCAL and MDL primarily on early, middle, and late attention blocks, respectively.
Single-Subject
Multi-Subject
Method
Overall
DPG
ID>Sim
IP-Sim
AFS
AVG
DPG
ID-Sim
IP-Sim
AES
AVG
MOSAIC"
91.24
75.11
75.62
56.37
74.59
85.46
60.09
68.55
53.39
66.87
70.73
MOSAIC
96,55
81.98
80.92
60.77
80.05
88.94
69.90
74.27
55.02
72.03
76.04
MOSAIC 1OSSII
94.87
79.42
78.36
58.13
77.70
87.25
65.71
71.92
54.21
69.77
73.74
MOSAIC
93.02
76.84
77.11
57.42
76.10
86.27
62.35
70.48
53.92
68.26
72.18
We investigate the optimal placement of our proposed SCAL and MDL modules across different attention layers. Table TO presents ablation results comparing various layer configurations against our baseline model (MOSAIC*).
Overall effectiveness validation. Regardless of placement, all configurations consistently outper- form the baseline, demonstrating the robustness and universal applicability of our SCAL and MDL. design. This consistent improvement across different architectural positions validates the fundamen- tal effectiveness of our approach.
Early layers achieve optimal performance. The configuration applying SCAL and MDL primar- ily to early attention blocks (MOSAIC") delivers the best results, achieving overall score of 76.04 across both single- and multi-subject scenarios. This superior performance stems from the architec- tural characteristics of carly attention layers, which primarily handle global structure and semantic alignment. At this stage, reference-to fetrenncett--trreet attention can establish more accurate positional corre- spondences, facilitating robust cross-image semantic consistency and effective subject separation.
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Diminishing gains in deeper layers. Extending supervision middle and late attention blocks (MOSAIC**M and MOSAIC**mi) yields progressively smaller gains. This performance plateau oc- curs because deeper layers increasingly focus local details rather than global correspondences. The emphasis on fine-grained features in these layers makes global position mappings between refer- ence and target images less relevant. Consequently, applying our constraints at deeper layers creates a trade-off between detail enhancement and semantic consistency preservation, partially offssttting the benefits of our proposed losses.
Implications for design choices. These findings reveal that early attention layers provide the optimal insertion point for SCAL and MDL, effectively balancing semantic alignment with com- putational efficiency. This configuration maximizes improvements across all evaluation metries: text-image consistency (DPG), identity preservation (ID-Sim), subject disentanglement (IP-Sim), and overall aesthetic quality (AES).
A.1.7 COMPARISON WITH STRONG MULTI-SUBJECT BASELINES
To ensure comprehensive evaluation against state-of-the-art identity-preserving methods, we con- ducted supplementary comparisons against strong baselines including FastComposer [iioo et al.) (002)) Subject-Diffusion Ma al. (2024), and Face-Diffuser WWang et al. (2024). Following the official evaluation settings of Face- Diffuuiff we evaluated Identity Preservation (IP) and Text Con- sistency (PC) under both Single-Subjer and Multi-Subject settings.
The quantitative results are presented in Table 11 MOSAIC exhibits significant advantages over these methods:
Single-Subject Scenario: Our method achieves an IP score of 0.785, substantially outper- forming Face-Diffuser (0.708) and FastComposer (0.514).
Multi-Subject Scenario: The advantage is even more pronounced. MOSAIC achieves an score of 0.712, far surpassing Pace-Diffuser 0.594.
We attribute this superior performance to our fine-grained alignment (Lscaa) and spatial decoupling mechanism (LMD), which effectively resolve feature confusion between multiple subjects, thereby achieving state-of-the-art levels in both identity preservation and text consistency.
Table 11 I1: Quantitative comparison with strong multi-subject baselines. We report Identity Preserva- tion (IP) and Text Consistency (PC) scores under zero-shot settings. MOSAIC significantly outper- forms FastComposer and Face-Diffun Mife-Diffuser, particularly in multi-subject identity preservation.
Single-Subjer
Multi-Subject IP
Methods
IP f
PC t
PC t
FastComposer (zero-shot)
0.514
0.243
0.465
0.233
Subject- DifffcccDiffusinn (zero-shot)
0.605
0.228
0.435
0.210
Face-Difhe Diffuser (zero-shot)
0.708
0.325
0.594
0.320
MOSAIC (zero-shot)
0.785
0.362
0.712
0.358
A.1.8 IMPACT OF LORA RANK
To determine the optimal model capacity for capturing fine-grained semantic correspondences, we conducted a detailed ablation study on the LoRA rank setting, evaluating performance on the Dream- Bench multi-reference scenario with ranks set to 32, 64, 128, and 256. As presented in Table [2] we observe two distinet phases regarding performance scaling. Initially, a Performance Growth phase is evident from Rank 32 to 128, where increasing the rank expands the learnable parameter capac- ity, leading to a significant upward trend across all metrics (CLIP-I, CLIP-T, DINO). This indicates that a sufficient parameter budget is essential for the model to learn complex spatial decoupling and precise point-wise alignment. However, this is followed by a Performance Saturation phase from Rank 128 to 256, where performance gains become negligible (e.g., CLIP-I improves by only 0.03), exhibiting clear diminishing returns. Consequently, considering the trade-off between performance gains and training costs (VRAM usage and computational load), we selected Rank 128 as the
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Table 12: Ablation study on LoRA rank. We compare performance across different rank settings (32, 64, 128, 256) on DreamBench. Rank 128 achieves the best balance between performance and parameter efficiency, while Rank 256 offers only marginal improvements.
LoRA Rank
CLIP-I
CLIP-T
DINO
32
74.30
31.55
54.02
64
75.71
32.10
55.90
128
76.30
32.40
56.83
256
76.33
32.45
56.85
optimal default setting, as ensures sufficient capacity for high-quality alignment while avoiding unnecessary resource overhead.
Table 13: Inference speed comparison of consistency-based models on NVIDIA H20 GPU at 512x512 resolution. MOSAIC achieves competitive performance while supporting multiple ref- erence inputs.
Method
UNO
DreamO
Verse
MOSAIC
Inference Speed (s/it)
2.05
1.16
1.87
1.53
Computational cost analysis of SCAL with different sampling points. We measure the training time overhead per training step. p(k) = 150 represents the optimal balance, offering sig- nificant performance gains with acceptable computational cost, whereas p(k) = 200 introduces excessive overhead.
Table 14:
Sampled Points
Training Time per Step (s)
Inference Time per Step (s)
0
2.85
1.53
50
3.10
1.53
100
3,36
1.53
150
3.68
1.53
200
4.70
1.53
A.1.9 TRAINING COMPLEXITY
While the previous section confirms MOSAIC's zero-overhead advantage during inference, also provide a detailed analysis of the training computational cost relative to the number of sampled points (P(R)).
As analyzed in the previous ablation study (Sec.A.1.5), increasing ((k) improves performance up to saturation point. To balance these gains with efficiency, we measured the training time con- sumption per-step of the SCAL. and MDL. As shown in Table 14 setting p(k) = 150 (our default configuration) results in manageable time overhead of 3.68 s/step, representing merely 29.1 % increase in training cost compared to the baseline. In contrast, increasing to 200 points leads to sharp spike in computation time (4.70 s/step) while yielding diminishing performance gains. This analysis justifies our choice of 150 points as the optimal trade-off between training efficiency and generation quality.
A.2 MORE QUALITATIVE COMPARISONS
We provide extensive qualitative results in Fig. 15 Fig. 16 and Fig. 17 to further demonstrate the superiority of our approach in maintaining visual coherence across diverse multi-subject scenarios. Our method consistently outperforms ofate-of-the-an approaches in several critical aspects:
Fine-grained Detail Preservation. In row of Fig. I6 while DreamO Mou et al. (2025a) and
OmniGen2 Wu et al. (025a) successfully preserve textual content the bowl, only method
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w/o LIcc & Lyd
w/o Lrd
MOSAIC
Denoising timesteps
dog is playing with toy car in the room.
Figure 7: Step-by-step denoising visualization mechanism effects. We compare the generation process across three settings: (Left) Baseline: Fails to preserve fine-grained identity details of the car. (Middle) w/ LscA only: Successfully aligns identity features but suffers from feature en- tanglement, where the yellow color from the car bleeds onto the dog (highlighted by the red box). (Right) MOSAIC: With the addition of LAD, the model achieves both precise identity preservation and spatial disentanglement, ensuring correct color attribution for both the dog and the car.
curately reconstructs both the intricate shape and surface texture of the vase, demonstrating superior fine-grained detail transfer capabilities.
Subject Completeness and Consistency, Row 3 of Fig. 16 highighss common failure modes in ex- isting methods: MS-diffusion Wang et al. (2025) and Verse Chen et al. (2025) suffer from object repetition omission artifacts, while UNO and OmniGen2 exhibit significant facial consistency degradation. In contrast, our approach accurately renders all three reference objects while maintain- ing overall visual coherence.
Cross-Subject Interference Mitigation. Across examples, our method demonstrates robust performance in preventing attribute bleeding between subjects, maintaining distinct identity charac- teristics for each reference entity without compromising generation quality.
A.3 SCALABILITY TO CHALLENGING MULTI-SUBJEC SCENARIOS.
Fig [7 presents challenging 4+ subjects generation results that highlight the scalability advantages of our approach. In the superhero scene (row 1), existing methods struggle with identity confu- sion and incomplete subject rendering: MS Diffusion Wang et al. (005) loses Captain America's distinctive shield design. UNO Wu et al. (2025b) fails maintain Iron Man's armor details, and XVerse Chen et al. (2025) exhibits significant pose distortions. Our method successfully preserves all four superheroes with accurate costumes, poses, and distinctive characteristies. Similarly, in
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the multti-bjjet scene (row 2), while competing methods suffer from object omission and attribute mixing (incorrect colors or textures bleeding between subjects), our approach maintains perfect sub- ject completeness and identity consistency across all 5+ reference entities. This demonstrates method's unique capability to scale beyond the typical 2-3 subject limitation of existing approaches, enabling complex real-world multi-subject generation scenarios.
A.4 GENERATION DIVERSITY ANALYSIS
common concern in personalization tasks is the trade-off between fidelity and diversity: stronger identity constraints often lead mode collapse rigid copying of reference poses. To demonstrate that MOSAIC achieves high fidelity without sacrificing diversity, we conduct both quantitative and qualitative evaluations.
Quantitative Evaluation. We performed rigorous "intra-class diversity" test on the DreamBench multi-reference scenario. For each test case, fixed the prompt and reference images, generated outcomes using 10 different random seeds, and calculated the average LPIPS distance. As shown in Table [5] MOSAIC achieves the highest diversity score (52.15), outperforming existing SOTA methods.
Table 15: Quantitative diversity comparison. We report the average LPIPS distance calculated across images generated with fixed prompts but different random seeds. A higher LPIPS score indicates greater perceptual diversity.
Method
LPIPS (t)
MS-Diffusion
45.85
X Verse
48.55
DreamO
50.10
UNO
51.20
MOSAIC (Ours)
52.15
Qualitative Visualization. As illustrated in Fig. under fixed prompts and reference subjects, our model generates highly diverse outputs across varying seeds. For instance. the cat is generated with varying poses (sitting vs. lying down) and the desktop objects appear in different spatial layouts.
living room.
computer, lamp a toy on
Figure 8: Qualitative evaluation of generation diversity. We fix the text prompts and reference images while varying random seeds. (Left) The model produces diverse poses for the cat and varying viewpoints for the chair. (Right) The spatial layout and lighting of the desktop scene vary significantly across samples. This confirms that MOSAIC preserves high subject fidelity without rigidly overfitting to the reference image geometry.
Mechanism Analysis. We attribute this superior balance to the nature of our Point-wise Semantic Constraint: Unlike global feature alignment strategies that constrain the entire latent space, LSCA only anchors key semantic feature points. This leaves the diffusion model with significant freedom generate non-critical areas such backgrounds, lighting, and specific poses. Simultane-
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3
A clock is hang on the wall in the living room.
A woman holding bouquet of flowers
sits on park bench.
A television is placed on the table with smiling man in a t-shirt walk in the street. toy next to
Figure 9: Robustness evaluation under challenging input conditions. We test MOSAIC's perfor- mance when reference images are imperfect. (Top Row) Partial Occlusion: Even when reference subjects are significantly occluded (e.g.. a masked clock or face covered by hand), the model suc- cessfully reconstructs complete, identity scenarios combining high-quality and low-quality inputs (e.g.. clear TV paired with blurry teddy bear), the model generates all subjects with uniform high fidelity. This demonstrates the robustness of our semantic feature alignment mechanism against input degradation.
subjects. (Bottom Row) Mixed Resolution: In
ously, LMD prevents feature entanglement between subjects, avoiding "mode collapse" where the model might otherwise revert to a confused, average representation.
A.5 ROBUSTNESS TO CHALLENGING INPUTS: OCCLUSION MIXED RESOLUTION
Real-world applications often involve imperfect reference images, such subjects being partially occluded or having varying resolutions. To evaluate MOSAIC's robustness under these challenging conditions, conducted specific visualization experiments shown in Fig. 9
Handling Partial Occlusion. The top row of Fig. demonstratess scenarios where reference sub- jects lose significant visual information (e.g., a clock partially masked by gray patch, or woman covering one eye). Despite these occlusions, MOSAIC successfully reconstructs the complete sub- jects with consistent identity. This capability suggests that our model does not mechanically copy pixels but rather anchors the subject's identity using key semantic points from unoccluded regions, effectively completing missing information.
Handling Mixed Resolution. The bottom illustrates mixed sixeed-ualityy scenarios, such pairing a high-resolution TV with a blurred, low-resolution teddy bear. Despite the disparity in input quality, the generated results exhibit uniform high fidelity for all subjects. This proves that our alignment mechanism can extract core semantic features even from degraded inputs, aligning them within a unified high-quality feature space.
A.6 FAILURE CASE ANALYSIS AND LIMITATIONS
While MOSAIC demonstrates state-of-thed the--rtttettttt performance across a wide range of benchmarks, we conduct transparent analysis of failure cases highlight current limitations. As illustrated in Fig. 10 our model faces challenges in scenarios involving extreme spatial complexity:
Complex Physical Containment. The first of Fig. 10 presents a nested containment task: "A plushic toy inside glass jar, which is placed inside a box." While MOSAIC successfully generates all three elements and correctly places the jar within the box, maintaining the visual consistency
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MOSAIC
UNO
XVerse
Omini6en2
MS-Diffusion
skuthi splice
A oit min in the Feer rwn rerprrikke i in momion Serumd to and the londsagg clear dag fytting
Figure 10: Failure case analysis in highly complex scenarios. We visualize two representative lim- itations: (Top Row) Complex Physical Containment, where the model captures the nested spatial relationship but suffers from visual consistency degradation of the outer container (the box); and (Bottom Row) Fine-graines Spatial Layouts, where strict adherence complex positional con- straints for multiple subjects not perfectly maintained. Despite these limitations, MOSAIC still exhibits superior subject preservation and prompt adherence compared to baselines.
of the outer container (the box) remains challenging. Due to the complexity of the nested com- position, the box occasionally exhibits structural distortions loss of texture details compared to the reference. However, compared to baseline methods like UNO and XVerse-which often fail to generate the container entirely blend objects together-MOSS structural coherence.
still achieves the best overall
Fine-grained Spatial Constraints. The second row demonstrates complex spatial layout task requiring specific positioning of four animals (cat, dog, rabbit, deer). While MOSAIC generates all subjects with high fidelity, it occasionally fails strictly adhere rigorous geometric constraints (e.g., specific "left-right" alignment), resulting in minor semantic mismatches regarding position. Despite this, it significantly outperforms competitors that frequently suffer from severe subject omis- sion or identity confusion in such high-density scenes.
B DETAILED DATA CONSTRUCTION AND COMPARISON
B.1 DATA CONSTRUCTION PIPELINE
Our data construction follows a systematic five-stage pipeline designed to generate high-quality multi-subject training pairs with validated semantic correspondences.
Stage Multi-Subject Prompt Generation. We design structured image generation templates and leverage GPT-4o to produce diverse multi-object prompts. These prompts comprehensively span various combinations of humans, animals, objects, and their interactions, ensuring thorough coverage of complex multi-entity scenarios encountered in real-world applications.
Stage 2: High-Quality Image Synthesis and Filtering. The generated prompts are fed into state- of-the-art text-to-image models to synthesize corresponding images. To ensure data quality and fi- delity, we implement comprehensive multi-criteria filtering pipeline: (1) We employ CLIP+MLPP based aesthetic assessment model Schuhmaan (2022) to discard images below predefined aesthetic thresholds. (2) We utilize GroundingDINO Liu et al. (2023) to verify high-confidence detection of each entity mentioned in the prompt, filtering instances where subject regions too small or inadequately captured.
(3) We compute CLIP-based prompt-image semantic similarity scores to remove images that fail to meet semantic alignment thresholds.
Stage 3: Subject Detection and Segmentation. We apply Lang- SAM Kinillov et al. 2003) for robust open-vocabulary detection and segmentation across all synthesized images. This enables
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precise localization and extraction of all subjects regardless of semantic category, establishing foundation for reliable correspondence matching in subsequent stages.
Stage 4: Multi-View Augmentation. To enhance dataset diversity and ensure comprehensive ap- pearance coverage across varied viewpoints and poses, we incorporate FLUX Kontext Labs et al. 2025 for viewpoint correction while preserving semantic coherence.
Stage 5: Semantic Correspondence Construction and Quality Assessment. During image pair construction, employ rigorous cross-validation to maximize correspondence reliability and mantic consistency. For each reference-target pair, we apply targeted masking to isolate reference subject regions in the target image, minimizing interference from other entities. We utilize both DIFT Tang et al.] (2023) and GeoAware-Se ZZhang et al. (2024a) to establish fine-grained token- level correspondences, aggregating tokens mapped to the same region into sampling pools.
Stage 6: Dataset Quality Assessment.
To rigorously ensure the quality and accuracy of our dataset, implemented a comprehensive two-stage assessment mechanism:
Automated Vonss-valia,ion.. As detailed in Appendix B.1 (Line 786), we eschew reliance on single model in favor of an "intersection strategy" to filter out noise. Specifically: We utilize two distinct feature extraction methods: DIFT Tang al. (2023) and Geo-Aware Zhang et al. (2024a) features. A sample is retained only when the corresponding points extracted by both methods exhibit high spatial consistency (i.e., falling within intersection region). This approach leverages the complementarity of different algorithms to automatically eliminate the vast majority of potential matching errors.
Human-in-the-logp Verification. To further quantify the dataset's accuracy, we conducted a rig- orous manual inspection process: We performed stratified sampling on 10% of the full dataset, ensuring balanced distribution across both single/multi-refrrnnee scenarios and various object categories." Human annotators manually verified the semantic consistency the matched points in these samples. The final statisties indicate that the error rate is strictly controlled within 3%.
Through this strict automated filtering and statistical human verification, we strongly guarantee the high quality and reliability of the SemAlign-MS dataset.
50-196 (261))
(30117%)
Subject Deenty
Prompt
(7%)
100-150 (EN4
150-200 (10%)
Figure 11: Detailed distribution and diversity statistics of the SemAlign-MS dataset. The visual- izations illustrate the dataset's balanced construction across three key dimensions: (a) Reference Balance: The dataset maintains an even distribution of subject counts, ranging from single-subject scenarios to complex multi-subject compositions (up to six references). (b) Category Diversity: The
data covers major semantic categories including Humans (H), Animals (A), and Objects (O), featur- ing rich cross-category interaction combinations (e.g.. H+A, H+O). (c) Prompt & Subject Complex- ity: The distribution of prompt lengths demonstrates coverage from short labels (<50) to complex long- forg-form descriptions (>200), alongside wide variety of fine-grad iin-inaieed subject classes
B.2 DATASET STATISTICS AND VISUALIZATION OF FULL ANNOTATIONS
Dataset Statistics. Following these comprehensive assessment and filtering procedures, our pipeline yields high-quality dataset comprising 1.2M reference-target image pairs with vali- dated semantic correspondences, providing precise supervision for multi-subject generation tasks. To comprehensively analyze the coverage and potential biases of our constructed dataset, present
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tall grandfather clock stands beside a upholstered armchair creating warm. inviting atmosphere comfortable home.
womon and on eiderly man stand of on ort gellery, gezing at large pointing, She holds natepad and gestures enthusiasticaly, while discussing the artwork He listens thoughffully with arms crossed, offering insights.
A young wemon and boy stond together, each grappnng a handle of a large aultcase covered in cslorful trovel stickers. The boy looks up eagerly while the woman smiles with encouregement
prepare lift
Figure 12: Visualization of full annotation examples from SemAlign-MS. We showcase the dense semantic correspondences established by our pipeline across diverse scenarios: (Left) A dual-object scene (Grandfather Clock Armchair); (Middle) A dual-human interaction (Woman Elderly Man); and (Right) A complex triple-subject composition (Woman + Boy + Suitcase). The colored lines represent validated point-wise semantic matches between reference subjects and the generated target image. Crucially, notice how the correspondences are strictly isolated for each subject (e.g.. separate mapping flows for the boy vs. the suitcase), ensuring precise multi-subject supervision without feature entanglement.
detailed distribution statistics in Figure 11 The statistics reveal that SemAlign-Ms is constructed with a deliberate focus on balance and diversity across three critical dimensions:(a) Reference Bal- ance. Unlike prior datasets that predominantly focus on single-subject generation, our dataset main- tains a robust balance between single-subject and multi-subject scenarios. As shown in Figure lT a)) Single, Dual, and Triple-subject samples each constitute approximately 20% of the dataset, ensuring the model's capability to generalize across varying subject densities. We also include challenging high-density samples (Quad, Penta, and Hexa subjects) further test the model's limits in complex composition. Category Diversity. To prevent domain bias, we ensure a wide semantic cover- age across three core categories: Human (H), Animal (A), and Object (O). Crucially, the dataset is not limited to isolated categories but features a rich array of cross- coosscc interactions. The distribution in Figure (bb) highlights substantial proportions of heterogeneous combinations (e.g., Human-Animal. Human-Objes Object interactions), enabling the model to learn disentangled representa- tions for diverse semantic concepts rather than overfitting to specific domains like faces. Prompt & Subject Complexity. We also emphasize diversity in textual complexity and fine-grained subject classes. As illustrated in Figure c),, the prompt lengths follow a broad distribution, ranging from concise labels (< 50 tokens, 17%) to detailed, long- formform& descriptions (100 150 tokens, 31 %; > 200 tokens, 7%). This variance ensures the model remains robust to different user prompting styles. Furthermore, the dataset encompasses vast taxonomy of fine-grained subjects (e.g.. spe- cific professions, animal breeds, and object types), providing the necessary granularity for precise personalization.
Visualization Full Annotations. To intuitively demonstrate the granularity and accuracy of our data annotations, we present detailed visualizations of the established semantic correspondences in Fig. [2 These examples illustrate how our automated pipeline generates dense, point-wise map- pings for complex multi-subject scenarios. As shown, our method successfully establishes specific correspondences for each individual entity (e.g., distinguishing between the woman, the boy, and the suitcase) while maintaining strict semantic consistency. These high-quality, dense annotations serve as the foundational ground truth for our proposed point-wise alignment (LSCA) and spatial disentanglement (TMD) mechanisms.
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Object 9210%
Enger SAN
Single-Subject
Human 1.3%
Caliee 12%
12
22
Figure 13: Comparative analysis of data distribution. We compare our SemAlign-Me MS with Subject- 200K Tan et al. (2025) and X21-subject-driven Xiao et al. (2024). (Left) Subject-200K is strictly limited to single-subject samples with strong object-centric bias. (Middle) X2l-sunject supports up to three subjects but shows skewed distributions in category combinations (e.g., dom- inating H+H+O patterns). (Right) Our SemAlign-Mi demonstrates superior balance, featuring a uniform distribution across subject counts (I to 6) and diverse, unbiased semantic interactions.
B.3 COMPARISON WITH EXISTING DATASETS.
To demonstrate the uniqueness and superiority of SemAlign-MS, we conduct a comparative distri- bution analysis against representative datasets: Subject-200K Tan al. (2025) widely used single-subject dataset) and X21-subject-driven Xiao et al. (0024) (a multi-subject dataset). As visu- alized in Fig. 13
Subject-200K (Left) is predominantly composed of single-subject samples (100%), with a heavy bias towards the Object category (92.0%), lacking human or animal interactions.
X21-suhject-driven (Middle) introduces multi-subject scenarios but is limited to maxi- mum of three subjects. Moreover, exhibits significant bias in category combinations; for instance, in triple-subject scenarios, the specific "Human+Human--Ohjer" combination dominates (58.6%), limiting the diversity of semantic interactions.
Aignn-S (Right) achieves a significantly more balanced distribution. covers a wide range of subject counts from I to 6 (including challenging high-density scenes like Hexa- subject) with an even ratio (~20% for 1-3 subjects). Furthermore, it features a rich and uniform distribution of cross-category interactions (c.g.. Human-Anima Animal-Object) without over-relying on specific patterns.
This comparison confirms that SemAlign-MS offers superior diversity and balance, providing more robust foundation for training generalizable multi-subject generation models.
C DETAILED STATISTICS OF EVALUATION BENCHMARKS
To ensure a fair and unbiased assessment of model performance, we utilize two carefully curated benchmarks: DreahBench and X VerseBBnnhh. As illustrated in Fig. [4] both benchmarks are con- structed with strict controls on sample size, category distribution, and prompt diversity to eliminate potential data bias.
DreamBench Statisties (Left). DreamBench is strictly balance the ratio between singlc-subject and multi-subjed scenarios. As shown in the pie chart, the dataset is split evenly:
Single-Subject (50%): Comprises Object (35.0%) and Animal (15.0%) categories, cover- ing fundamental subject generation capabilities.
Multi-Subject (50%), )) Evenly distributed across three interaction types: Animal+Animal Animal+ and Object+Object (16.7% each).
This 50/50 split ensures that metrics are not skewed by simple single-subject cases, providing a rigorous test for multi-subject consistency.
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H
Mutti-Sujjet
Segle-Subicc
Figure 14: Statistical distribution of evaluation benchmarks. (Left) DreamBench: The benchmark maintain a strict 50%-50% balance between Single-Subject and Multi-Subject scenarios to ensure unbiased evaluation of basic and compositional capabilities. (Right) XVerseBench: The bench- mark comprises 300 test cases with a difficulty gradient Single/Dual/Triphe subjects). It features highly uniform distribution across diverse semantic categories (Human, Animal, Object) and their combinations, preventing evaluation bias towards specific domains.
XVerseBench Statistics (Right). Designed to evaluate complex multi-subject interactions, X XVerseBench consists of 300 distinet test cases structured with difficulty gradient:
The benchmark includes 90 Single-Subject, 120 Dual-Subject, and 90 Triple-Subject samples, testing the model's scalability from subjects.
Sample Size Distribution:
Category Combination Balance: To avoid bias towards specific domains (e.g., human faces), we ensure a uniform distribution of category combinations. For instance, the Dual- Subject set (120 cases) is equally divided into combinations (c.g.. Human+Human, Hu- man+Object, Animal+Object, etc.). with 20 cases each. Similarly, the Triple-Subject set covers diverse permutations (e.g., H+H+A, H+A+A) comprehensively assess interaction handling.
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MOSAIC(Ours)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
classical bust with intricate features rests on a pedestal.
MOSAIC(Ours)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
A robot toy is placed on the ground, with toys scattered around it.
Figure 15: Qualitative comparison single subject driven generation.
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MOSAIC(Ours)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
A bowl and vase are the dining table in the living room.
MOSAIC(Ours)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
A camera is placed on a armchair.
MOSAIC(OUrs)
M.S-Diffusion
UNO
DreamO
XVerse
OmniGen2
A man wearing a cap holds teddy bear in his hand.
Figure 16: Qualitative comparison on multi-subject driven generation.
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MOSAIC(OUrs)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
Four superheroes stand side by side on a city street. Behind them, modern skyscrapers tower under a bright, clear sky.
MOSAIC(Ours)
MS-Diffusion
UNO
DreamO
XVerse
OmniGen2
woman stands on a sunlit city street, wearing jeans adorned. She carries a handbag over her shoulder. Beside her suitcase, At her feet, dog lies patiently.
Figure 17: Qualitative comparison 4+ subject driven generation.
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Reviews:
[{"review_id": "eHffXZKdWy", "reviewer": "Reviewer_cdZj", "summary": "tries to improve multi-subject personalized generation, by both aligning semantics for each subject/identity (knowing which regions in the generated image should attend to which parts of each reference), as well as disentangling each reference by pushing different subjects into orthogonal subspaces to minimize feature interference. They resolve the alignment challenge with a fine-annotated dataset and correspondence loss, and approach the later with a disentanglement loss. While both aspects are been explored and methods and datasets are proposed to resolve the challenges, MOSAIC does repurpose the similar ideas in a novel way.", "questions": "Please see details in Weaknesses. I think this work presents new insights and shows technical novelty, but there also exists confusions and concerns regarding overclaims and experiment details.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The proposed correspondence loss for alignment is attention-based, which seems principled and interesting to me. \n\n2. The multi-reference disentanglement loss is based on **orthogonality**, which is a fairly new perspective in multi-subject generation. This perspective is more theoretically grounded and can also inspire more theories and algorithms, the loss formulation can be considered as a novel contribution to how we enforce it in diffusion models.\n\n3. The construction pipeline of SemAlign-MS is sound and inspires future development of new benchmark too.", "weaknesses": "1. SemAlign-MS lacks detailed information, e.g. I don't find number of samples, splits, distributions, etc.? To claim a dataset contribution, it is crucial to include these details, as well as examples with full annotations and comparisons with a few similar datasets.\n\n2. Both the reference alignment and multiple-reference disentanglement have been proposed and studied. To me, the intuition and conceptual novelty is modest, e.g. [1] and its variants proposes similar alignment and [2] leverages orthogonal adaptation for multiple conditioning. I acknowledge that this work has technical novelty though. \n\n3. Table 3: Can you add the ablation setting with MD and without SCA? This helps me compare which contributes more. \n\n4. Can MOSAIC works for/generalizes to other datasets/tasks besides SemAlign-MS? The authors claim that MOSAIC achieves SOTA on multiple benchmarks but I don't find such experiment results. Also, I think the correspondence loss relies on fine-grained annotations, making it hard to decouple and apply to other datasets/tasks?\n\n5. While the results look good, there lacks quantitative/qualitative evaluation of generation diversity. Because both additional losses are enhancing fidelity, it inevitably makes the training weighs identity preservation more.\n\n[1] Xiao, Guangxuan, et al. \"Fastcomposer: Tuning-free multi-subject image generation with localized attention.\" International Journal of Computer Vision 133.3 (2025): 1175-1194.\n\n[2] Po, Ryan, et al. \"Orthogonal adaptation for modular customization of diffusion models.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eVhTh0nLHp", "reviewer": "Reviewer_pkpp", "summary": "The paper introduces MOSAIC, a multi-subject personalization framework that supervises reference to target attention with SCAL and reduces cross-subject leakage by MDL. It also releases SemAlign-MS, a dataset of dense point correspondences enabling effective attention-level supervision. On DreamBench and XVerseBench, MOSAIC achieves SOTA gains, for scenes with 4+ subjects.", "questions": "1. Can you report results on a second backbone (e.g., SDXL) to demonstrate novelty of SCAL/MDL?\n\n2. How do performance and training cost scale with the number/quality of correspondence points?\n\n3. How is quality check of the SemAlign-MS dataset?\n\n4. Any failure cases for MOSAIC still struggles?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper represent the multi-object personalization as representation optimization problem. Then introduce two different losses for the combined target. The storytelling is clear and straightforward.\n\n2. The paper prove the 4+ object personalization tasks with less degradation. May apply for complex personalization cases.\n\n3. The two losses introduced is well-defined and easy to follow.", "weaknesses": "1. The comparisons between the FLUX-based model to other baseline finetuned from SDXL may not be fair. While the increasing on performance is rather marginal. The method may works for other backbones as well since the main contribution is two of losses. It will be better to see the improvement across conventional backbones.\n\n2. The quality of SemAlign-MS relies on the introduced construction models. It is not clearly how accurate the quality of the dataset itself.\n\n3. Cost of computation complexity has not been clearly discussed.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "keZLD9R6tP", "reviewer": "Reviewer_XamC", "summary": "This paper proposes the MOSAIC framework for the multi-subject-driven text-to-image generation task. Its core innovations are as follows:\n1. Correspondence-Aware Alignment (SCAL): It utilizes a cross-image semantic correspondence mechanism to accurately map the feature spaces of different subjects to the target generation positions, avoiding subject localization and semantic confusion.\n2. Multi-subject Disentanglement Layer (MDL): It achieves disentanglement between subjects at the feature layer, thereby enabling precise control over the appearance and position of each subject.\n3. It constructs the SEALIGN-MS dataset, ensuring the accuracy of training supervision through geometric consistency and semantic filtering.\nIn the two benchmark tests of DreamBench and XVerseBench, MOSAIC outperforms existing methods in multiple metrics (CLIP-I, CLIP-T, DINO, DPG, ID-Sim, IP-Sim, AES).\nOverall, the paper is relatively comprehensive in terms of data construction, algorithm design, and experimental validation, with the goal of improving the controllability and consistency of multi-subject generation.", "questions": "1.The paper lacks quantitative analysis of inference efficiency and complexity.\n2.In the Evaluation Setting, what are the specific details of the sample size, category distribution, and diversity of text prompts in DreamBench and XVerseBench? Is there any data bias that may affect evaluation fairness?\n3.Are the category and scene distributions of SEALIGN-MS balanced?\n4.Can step-by-step visualizations of the effects of MDL and SCAL on the generation results be provided in the appendix to facilitate a more intuitive understanding of their roles?\n5.What is the basis for setting the LoRA rank to 128? Are there any ablation experiments on the impact of different ranks such as 32, 64, 256 on performance?\n6.Can MOSAIC maintain consistent generation quality in multi-subject scenes when one reference image is high-resolution and another is low-resolution, or when some subjects are partially occluded?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. It constructs a high-quality SEALIGN-MS dataset, improving the quality of supervision signals.\n2. It introduces DIFT and GeoAware-SC for refined token-level correspondence, effectively alleviating the subject confusion issue in the multi-subject generation process.\n3. Its performance in multiple target generation tasks reaches the state-of-the-art level.\n4. The framework design can be extended to other conditional generation tasks, such as multi-person synthesis and television content generation.\n5. SCAL is responsible for accurate mapping, and MDL is responsible for isolating interference; their combination significantly improves the quality of multi-subject tasks.", "weaknesses": "1. High computational complexity and lack of lightweight property: The corresponding point matching and cross-attention supervision in SCAL increase additional computational overhead, which may affect the inference speed.\n2. The effect of MDL depends on mapping accuracy: If the correspondence of SCAL is inaccurate, the isolation effect of MDL may also be affected, leading to the contamination of subject features.\n3. Dataset bias towards specific domains: Whether SEALIGN-MS has domain bias lacks distribution analysis and discussion.\n4.The paper provides limited qualitative comparisons but lacks a systematic analysis of MOSAIC’s failure types such as local artifacts, semantic mismatch, and color shift, which limits the assessment of its robustness.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "p4v1RMunLQ", "reviewer": "Reviewer_Z5Z9", "summary": "The paper proposes MOSAIC, a representation-centric framework for multi-subject image generation that enforces explicit semantic correspondences and orthogonal feature disentanglement to prevent subject mixing. It introduces SemAlign-MS, a dataset with fine-grained, point-to-point correspondences between multiple reference subjects and target regions. Built on this, a semantic correspondence attention loss aligns each generated region to its designated reference parts, improving subject consistency and spatial localization. Besides, it also develops multi-reference disentanglement loss to prevent feature interference. Overall, the work reframes multi-subject generation as a representation-level alignment problem and delivers both the data and objective needed to make precise alignment feasible.", "questions": "Please see the weakness. \n\nIf the authors can thoroughly address all the listed weaknesses, I would be inclined to raise my score.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. New fine-grained dataset: Introduces a meticulously annotated, point-to-point multi-subject correspondence dataset (SemAlign-MS) that fills a clear gap and is poised to catalyze progress in the community.\n\n2. Loss tied to correspondences: Leverages those point-to-point annotations to design a Semantic Correspondence Attention Loss that enforces precise regional alignment, improving subject identity fidelity.\n\n3. Promising quantitative results: Reports strong quantitative gains that indicate the approach’s effectiveness for multi-subject consistency and localization.", "weaknesses": "1. Incomplete baselines: Missing comparisons to strong multi-subject/identity methods such as FastComposer [1] and Face-Diffuser [2], and so on.\n\n2. Limited image-quality evaluation: The paper does not report results on widely used VLM-based metrics (e.g., UnifiedReward and HPSv3), undermining claims about perceptual quality and semantic fidelity.\n\n3. Multi-person degradation: Quality drops when reference images contain multiple people (e.g., Fig. 4, last row), indicating instability under higher human-centric subject density.\n\n4. Dataset analysis gaps: Lacks statistics visualizations for SemAlign-MS (e.g., subject category distribution, subjects count distribution per sample, prompt length), obscuring coverage and potential biases.\n\n\n[1] \"Fastcomposer: Tuning-free multi-subject image generation with localized attention.\" IJCV (2025): 1175-1194.\n\n[2] \"High-fidelity person-centric subject-to-image synthesis.\" CVPR, 2024.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)