SIGMA-Gen: Structure and Identity Guided Multi-Subject Assembly for Image Generation
Authors: Oindrila Saha
Vojtech Krs
Radomir Mech
Subhransu Maji
Kevin James Blackburn-Matzen
Matheus Gadelha
Venue: ICLR 2026 Poster (2026)
Keywords: image generation
identity preservation
controllable generation
Primary area: generative models
TL;DR: SIGMA-Gen is a method for controllable, identity-preserving text-to-image generation with multiple subjects in one diffusion loop.

Abstract: We present SIGMA-Gen, a unified framework for multi-identity preserving image generation. Unlike prior approaches, SIGMA-Gen is the first to enable single-pass multi-subject identity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability to support user guidance at various levels of precision — from coarse 2D or 3D boxes to pixel-level segmentations and depth — with a single model. To enable this, we introduce SIGMA-Set27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k images. Through extensive evaluation we demonstrate that SIGMA-Gen achieves state-of-the-art performance in identity preservation, image generation quality, and speed.

SIGMA-GEN: STRUCTURE AND IDENTITY GUIDED MULTI-SUBJECT ASSEMBLY FOR IMAGE GENERATION
Oindrila Saha"
Vojtech Krs Adobe Research vkr Berr@adobe..c com
Radomir Mech
University of Massachusetts Amherst osaha@umass edu
Adobe Research rmech@adobe
Subhransu Maji
Kevin Blackburn-Matzen'
Matheus Gadelha
University of Massachusetts Amherst sma j unass. edu
Adobe Research nat zen adobe. com
Adobe Research gade Ladehhhbadbe com
spatial
speikl
Boscs
3D Boocs
3D Oboccu
Figure 1: SIGMA-GEN enhances controllability of text-to-image workflows by allowing users to prescribe both structure and subject identity. In the top row, RGB images are used to describe subject identities. 3D scene can be arranged by the user to describe the image structure; in these examples, meshes were automatically created using image-to-31D. The user then assign identities to each subject (colors representing the assignments) and generate images while precisely editing the 3D scene. In the bottom part of the figure, we show that SIGMA-GEN can also be applied to simpler modes of structure guidance 2D and 3D bounding boxes.
ABSTRACT
We present SIGMA-GEN, unified framework for multi-identity preserving im- age generation. Unlike prior approaches, SIGMA-GES is the first to enable single-pass multi-subject idemtity-preserved generation guided by both structural and spatial constraints. A key strength of our method is its ability user guidance at various levels of precision - from coarse 2D or 3D boxes pixel-level segmentations and depth with single model. To enable this, we introduce SIGMA-SET27K, a novel synthetic dataset that provides identity, structure, and spatial information for over 100k unique subjects across 27k im- ages. Through extensive evaluation we demonstrate that SIGMA-GEI achieves state-of-the-an performance in identity preservation, image generation quality. and speed. Code and visualizations at https: Sstpps///oiddr: lasaha github. ST ST/S/MMMAGGe///
support
Work done during internship at Adobe Research Equal advising

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INTRODUCTION
Recent advances in text-to-image generative models have enabled high-quality and diverse image
synthesis from natural language prompts (OpenAl 2023b Rombach et al. 2022] Peebles & Xie) 2023 Lipman al. 2022). However, these models still lack fine- fineggrinedd control, which limits their adoption in real-world creative workflows. In particular, users have little ability to (i) control the
identity of subjects and (ii) specify their arrangement within scene.
We argue that both forms of control are essential. For identity, we introduce the use of single-view RGB images as descriptors, inspired by artistic workflows where exemplar images define visual elements for integration. For layout, we advocate for 3D object representations with rendered depth serving as a natural proxy for position, orientation, and occlusion relationships. To accommodate different levels of user expertise and control, we propose single model that supports structural inputs at varying granularities: ranging from coarse 2D bounding boxes, to 2D masks, 3D bounding boxes, and per-pixel depth maps. This enables users to balance the ease of specification with the precision of control.
To the best of our knowledge, our work is the first to jointly support both structural guidance (cap- turing subject position, orientation, and occlusions) and identity guidance across multiple subjects within a single diffusion process. While prior works such as ControlNet (Zhang al.) 2023). Omini- Control v v1/v2 (Tan et al. 2024). have demonstrated the use of multiple control modalities, none can reliably enforce multiple identities alongside structural layout guidance. Previous methods can be adapted to perform iteratively to address the issue of generating images with multiple subjects, but typically incur high runtime costs and suffer from compounding degradation in image quality. In contrast, our approach enables simultaneous generation of multiple contrily- subjects, ar- ranged according to structural constraints, in single forward pass.
The key to our approach is large-scale synthetic data generation pipeline that automatically pro- duces aligned RGB images, depth maps, masks, and identity descriptors. The pipeline leverages recent advances in image generation, grounding, and depth estimation. Using this pipeline, we construct a dataset containing 27k images spanning over 100k distinct identities, providing the su- pervisory signal required for training. We evaluate our models against competitive basclines and demonstrate significant improvements in both quality and controllability. When using per-pixel depth and masks, our method improves overall image fidelity by 31 points, identity preservation by 2 points, and achieves 4x faster generation when synthesizing scenes with five or more distinct sub- jects. When analyzed under the coarser control modality of bounding boxes, our method improves overall image fidelity by 6 points and identity preservation by 11 points when synthesizing scenes with five or more distinct subjects. These results highlight the effectiveness of combining identity and structural guidance in unified text-to-image generation framework.
2 RELATED WORK
Structure control for image generation. Photorealistic image generation with diffusion models has paved the way for integrating conditions beyond text
(Ho et al.) 2020 Rombach et al. 2022
Podell et al. 2023 Peebles & Xie 2023 Lipman et al.
2022 ControlNet (Zhang al. 2023
and A2I-Adapter (Mou et al. 2024) introduce auxiliary conditioning pathways that enable structural guidance from inputs such as edges, depth, or segmentation. Other diverse of control such as bound-
ing boxes (Li et al.) 2023b Zheng et al. 2023 Wang et al. 2024c| Cheng et al., 2024 Chen et al.)
2023) have also been explored. However, these methods do not provide mechanisms for controlling subject identity.
2023], and 3D priors (Bhat et al. 2024 Omran et al. 2025
Ma al.
Subject personalization. DreamBooth (Ruiz et al. 2023) and Textual Inversion (Gal et al. 2022) showed that diffusion models can learn subject identity by training map of a few images of a given subject to unique text token. Subsequent methods (Kumari et al. 2023 Gu et al.) 2023 Liu et al.
2023a by Zhu et al. 2025 Jang al. 2024 extend personalization to multiple subjects within single image by incorporating positional information in text, adapting weights, or encoding spatial layout. However, all these methods require per-subject optimization to learn their appearance.
In-context generation. Another line of work trains diffusion models to incorporate subject im-
ages conditioning (Ye et al. 2023) Wang et al. 2024a Wei et al. 2023 With the advent of
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diffusion transformer (DiT) architectures, methods such GPT1-Image (OpenAl 2023a Nano Banana Google 2025). and Flux Kontext (BFLabs et al. 2025 have been developed to support identity preservation. Recent open-source methods (Mou et al. 2025 Wu et al. 2025 Guo et al.
2025 Chen et al. 2025) extend identity preservation on diverse tasks controlled by text. However, these methods struggle to personalize across multiple subjects, as training datasets typically include only small number of identities per image. Moreover, they lack support for structural control.
Subject insertion methods such as AnyDoor (Chen al. 2024 and Insert Anything (Song et al. 2025 provide mask- boosk-ggdded control but are limited to inserting single subject per generation. MS- Diffusion (Wang et al.) 2024b) and other recent works (Tan al. 2024 Tarrés et al. 2025 Li et al.
2023a Xiao et al. [025 Wang et al. 2025a) support different levels of spatial control, such as depth
or bounding boxes. However, these methods are either limited to single-subject personalization or struggle with multi-subject personalization involving many identities. This can also be attributed in part to the scarcity of multi- multi-subjeet training data. In contrast, SIGMA- GEN enables controllable multi-subject insertion, guided by identity images for each subject, within single generation step (Figure This leads to higher-quality and more coherent scene generation.
Subject personalization datasets. While several datasets support single-subject personalization- such as DreamBooth (Ruiz et al. 2023), CustomConcept (Kumari et al., 2023), Any Insertion (Song et al. 2025), and Subjects200k TTan et al. 2024) only few focus multiple subjects in image. Virtual try-on datasets (Choi et al. 2021 Liu et al. 2016) contain 2-4 identities per image, includ- ing person identity and garments, but limited to that domain. MultiWine (Tarés al.) 2025)
proposed a general-purpor or through manual annotation. In contrast, we present the 3IGMA-SET276
dataset with up to two identities per image, obtained either from videos synthetically gen- erated dataset with up to 10 subjects per image along with spatial controls and captions.
METHOD
Our objective is to generate an image I that includes subjects 81, 82.... 8n, E S based on a prompt P. and controls C, such that all subject identities are preserved and are placed according to spatial controls C. To enable controllable generation with many subjects one shot, we create a first-of-its- kind, high-quality dataset ($ 3.T) containing multiple subject identities in each image. Furthermore, we propose a lightweight representation ($ 3.3) that enables multi sulte-- generation effectively and efficiently. Finally, dataset and proposed representation is used for finetuning models with
varying granularity of structural control ($ 3.4)
3.1 SIGMA-SET275 DATASET
We illustrate our pipeline for generating SIGMA-SET211 in Figure 2 For each target image, our dataset provides per-subject data including identity image, mask, depth and 2D/3D bounding box. We begin by prompting an LLM to produce an image-generation prompt describing multiple subjects against diverse backgrounds, along with subject and background captions. For each prompt, we generate the target image using an off-the-shelf text-to-image model. Next, we use grounded segmentation tool generate individual subject masks using the subject captions. We also employ a depth estimation model to predict the target image depth. Next, in key step of our pipeline, we repose the subjects using Plux-Kontex, (BFLaby Labs al., 2025) to obtain identity images with varying poses and lighting conditions. Finally, to enable coarser control than per-pixel depth, we fit 2D and 3D bounding boxes to the segmented subjects. Please refer to the Appendix for more details.
3.2 THE SIGMA-GEN ARCHITECTURE
SIGMA-GEN takes as input a prompt P, a multi-subject identity control image 1 and a spatial control image _e to generate image I. Both identity and spatial control images are encoded using the pre-existing VAE a diffusion transformer model. Inspired by OminiControl (Tan et al. 2024 adopt unified attention mechanism in which the noisy image latents X and conditions P. 1 are concatenated along the token dimension as [P, X, 1S 1*), enabling multi-modal attention from every modality to all modalities.
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LLM
Asaalo doiphin, ship's whool
death
3D fitting
Training sample caption caption
Estimate
Tull captions
Depth
bounding
boxes
bg captions
Segment
Repose
suilor; dolphing ship wheal
subject
subject idantities
terget
Figure 2: Pipeline for generating SIGMA-SET27K. Our fully automatic synthetic data genera- tion pipeline involves creating compositional prompts with an LLM, generating images from these prompts, segmenting to obtain subject crops, reposing the crops to produce identity images, and estimating depth and 3D bounding boxes. We also show an example of a training sample for fine control scenario using precise masks and depth. The routing mask is colored RGB for visual- ization purpose, the pixel values for the subjects being 10, 20, 30 in practice for this example.
3.3 REPRESENTING MULTI SUBJECT CONTROL
We decompose the spatial control C into two categories: routing control R. and structure control T. The routing control specifies where each subject should be placed in the image, while the structure control can define additional overall control for the scene such as depth. To efficiently represent the spatial controls C' E [R, I], we embed them into a single spatial control image _e with dimensions H W 3, matching the shape of the generated image T. We show an example of our control images in the right column of Figure 2 and in mask visualizations of Figure E
Let = { denote the set of subjects and R {R. ) denoting their desired spatial regions in the image domain. We define mapping function f + M. which assigns each subject 8; E to a unique pixel intensity m, E M. The control image 1 is constructed as 1 (x) = 1s E R.J. Thus, I with shape H W encodes the subject layout, mapping each pixel to its corresponding intensity m,, with representing background. We discuss how we ensure each pixel maps single subject in the next section.
We now construct a subject identity condition image 1 (see training sample in right column of Figure 2 This image provides explicit identity information of each subject, allowing each pixel in the routing control to be unambiguously associated with its corresponding subject. Formally, let each subject 8; be represented by an image fs, с R x W' x3 of fixed spatial resolution H' x W" We then define 1 by concatenating all subject images { J.N. along the height dimension which yields I ((N-H')x H'''''''''' x3, Thus the i-th H' W' block 1 corresponds directly to region R,, serving as a compact visual dictionary of subject identities and regions.
3.4 MULTIMODAL STRUCTURE CONTROL
We design a method capable of handling diverse control modalities-includdmn pixel-level masks or depth maps, 2D bounding boxes, and 3D bounding boxes-wiee within the same condition 10 by concatenating the routing control 1 and the structure control 1"_
The routing control image 1 can be created from pixel-wise region information R. However, the effectiveness of this control image relies on the precision of the masks. Accurate masks naturally handle occlusions and ensure that no two regions overlap. In contrast, coarser forms of control such as 2D or 3D bounding boxes, may produce ambiguous overlaps. crowded scenes, this of- ten results in partial or complete occlusion. To address this limitation while preserving an efficient representation which all spatial conditions are encoded within single image, we adopt a bidirec- tional compositing strategy. Specifically, we construct two routing control images Rume and 7 Rase by pasting the subject masks in ascending order of subject occurrence in 1s and descending or- der respectively. The mask values m, associated with each subject 8; remain constant across both constructions; only the order of composition is varied. This bidirectional compositing increases the chances of region being visible in either of the two routing control images. The structure trol IJ refers to depth in our formulation and can be precise depths or coarser depths such as 3D bounding box depths. We refer the reader to Figure El for examples of coarser control images.
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With the routing controls |Rae and R and structure control I" create the spatial control image _ Concat( , R I": channel) E R "xxx3 For the case where we have precise masks of each subject, both the routing control images the same Iu IRe This representa- tion leads us to having only two condition images a subject identity control image 1 and a spatial control image T which together can enable controllable multi-subject generation.
Identities
Depth, Masks
Iterative Insert Anything*
SIGMA-Gen
D2
fighter jet hanger
D8
ID4
fashion studio with pattorned walls
D2
ID4
ID3
DD6
ID7
ID5
bright classroom with large windows
Figure 3: Multi-subject generation with masks and depth. SIGMA-GEX outperforms basclines both in terms of image quality (see zoomed crops top-right) and subject identity preservation. For our case we prepend "Place these subjects in" to the prompts.
4 EXPERIMENTS
4.1
IMPLEMENTATION DETAILS
We adopt Flux. I Kontext [de]] as the base model for training our method. For spatial control, we use the same RoPE (Su al. 2023) embedding as that of the noisy image. For identity control, we also use the same RoPE embedding but set the first dimension to ones (instead of zeros) to differentiate it from the noisy image, similar Flux Kontext. Training is conducted on a single node with A100 GPUs in three stages, using a LoRA rank and alpha of 128, the Prodigy optimizer (Mishchenko & Defazio 2023), and a total batch size 8. In the first stage, we train for 30k steps on subset of data containing up to four subjects per image. The second stage trains for 20k steps images with three or more subjects, followed by a third stage of 20k steps on images with more than four subjects.
To develop unified coarse-to-fine spatial conditioning, inputs for each training example: (i) precise masks with depth, (ii) 3D bounding box masks with depth, or (iii) 2D bounding boxes. We further apply random dropping of one spatial condition channel with probability 0.1, as well as augmentations including random dilation of masks and bounding boxes and aspect ratio variation of bounding boxes by 1%, to improve robustness.
randomly sample one of three structural
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During training, retain only the depths of the subjects, masking all other regions zero. This mitigates the need to provide background depth at inference. For conditioning, we alternate be- tween full prompts and background prompts with equal probability, constructing them either "Place these subjects in <bg prompt> or "Place these subjects to compose: <full prompt>". For constructing the routing control image, assign region intensity in steps of 10, with the region for the first subject marked with 10, the second subject with 20 and so on, We use non-gray colors while depicting masks in our figures for better visualization.
4.2 EVALUATION
For evaluation, we construct dataset of 710 examples, including 200 single-subject personalized generation cases and 510 multi-subject cases. Dataset statistics are provided in the Appendix, and the data generated as described
3.1
We adopt DINO (Oquab et al., 2023) (DINO-I) and SigLIP (Zhai et al. 2023) (SigLIP-I) scores evaluate subject identity preservation via image-to-image similarity. For multi-subject cases, we crop the generated image around the bounding box of each subject and compute similarity with the corresponding identity image, finally averaging over each crop and subsequently over every image. Cropping reduces the presence of other distracting elements in the image while computing similarity. We also evaluate texi-to-image similarity using the SigLIP text-to-image score (SigLIP- T), which captures overall composition and background fidelity. We use SigLIP as it has been shown to outperform CLIP on fine-gra fine-grained understanding tasks.
When precise depth is provided control, additionally compute the mean squared error (MSE) between the depth of the original and generated images, restricted to the subject regions. Unless otherwise specified, our evaluations use only subject depths as structure control. Finally, we assess perceptual quality with CLIP-IQA (Wang et al. 2023) and MUSIQ (Ke et al. 2021
4.3 BASELINES
For single-subject personalization evaluation, we use OminiControl with its trained subject and depth LoRAs, well as UniCombine. We also adapt strong baseline, Flux Kontext, incor- porate depth guidance by attaching a depth ControlNet during inference. In addition, we design another strong baseline by combining the state-of-the thet mask- insertion method, Insert Any- thing, with a depth ControlNet and a depth-to-iman image on which subjects are inserted. We refer to this variant as Insert Anything* and use for both single- and multi-subject personalization.
tool (BFLabs, 2024) to generate the initial
For the multi-subject setting, iterative insertion is the only feasible baseline, as no prior work enables multi-subject personalized generation with precise masks and depth. We also adopt MSDiffusion as a baseline for the coarser control task of generation with bounding boxes, for both single- and multi-subject cases. In our setup, bounding boxes are provided as filled mask images (see B.4). pasted in ascending and descending order of subject occurrence in the identity image. This design choice enables unified control modality, whereas MSDiffusion instead constructs box embeddings directly from bounding box coordinates.
5
RESULTS
We compare SIGMA-GER with baselines both quantitatively and qualitatively. We further analyze different control types through ablations, showcasing emergent properties and applications.
5.1 COMPARISON WITH BASELINES
We present quantitative evaluation our model with different types of controls in Table II Using single trained model, we evaluate with (a) precise masks and depth, (b) 2D bounding boxes, uses only subject
and (c) 3D bounding box masks with depth. For this evaluation, SIGMA- depths, whereas other methods that accept depth provided with full image depths. As shown in Table 3. SIGMA- GEN with subject depths performs comparably to the setting when full depth provided. For the precise mask and depth case, we use background prompts for fair comparison

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Identities
coordinates
Masks
3D Box Masks
Depth
D1
D8
ID4
ID6
an engineer, cat, a star chart, a telescope, monitor, workbench, and a blusprint -in gerage with concrets floor SIGMA-Gen
MS-Diffusion
SIGMA-Gen
Figure 4: Multi-subject generation with coarse controls. Baseline fails maintain position or identity, while SIGMA- -GE adheres to both 2D and 3D bounding- coarse control. For our case prepend "Place these subjects compose: to the prompt.
Subject
Text SigLIP-T
Depth MSE
Quality
Method
Control
DINO-I
SigLIP-I
CLIP-IQA
MUSIQ
UniCombine
75.89
82.72
15.52
Depth
103.1
71.47
70.81
Single Subject
OminiControl
70.78
81.06
15.63
Depth
163.9
67.28
69.91
Insert Anything*
74.69
81.44
16.01
Mask, Depth
216.2
65.97
67.35
Flux Kontext*
79.96
83.22
15.99
Depth
112.4
66.30
64.93
SIGMA-GRE
81.04
83.99
15.91
Mask, Depth
70.2
70.65
70.87
MSDiflusion
74.83
82.65
2.41
Bbox
61.98
67.40
SIGMA-GEI
79.16
83.70
16.13
Bbox
70.82
70.75
SIGMA-GEN
79.98
84.00
16.03
Mask, 3D bbox
70.94
70.67
Multi Subject
Insert Anything*
72.72
75.58
17.66
Mask, Depth
203.4 26.35
44.41
48.86
SIGMA-GEN
74.54
77.82
17.73
Mask, Depth
72.64
73.21
MSDiffusion
63.28
69.06
11.20
Bbox Bbox
61.99
69.05
SIGMA-GEN
71.90
73.15
17.21
68.83
70.96
SIGMA-GEN
73.48
75.27
18.19
Mask. 3D bbox
72.45
72.55
Table Quantitative comparison with baselines. SIGMA- achieves competitive or superior performance in single-subject controllable identity-preserving generation, and significantly outper- forms baselines the multi-subject setting.
to the baselines, while for the coarser controls we use full prompts as the bounding box baseline MSDiffusion expects full prompts too.
Our method consistently surpasses all baselines in subject consistency, as reflected by DINO-I and SigLIP-I scores for both single- and multi-subject personalization. For image-to-text similarity (SigLIP-TT), our method outperforms all baselines except Insert Anything in the single-subject sce- nario. The stronger SigL.IP-T performance of Insert Anything* can be attributed to its use of depth-to-image model with full depth generate initial image for subject insertion. Our ablation with full depth (Table confirms that SigLIP-T benefits from access to full depth during generation.
Our method also achieves the lowest MSE between original and generated depths, demonstrating strong adherence to precise depth control while also supporting coarse depth guidance. terms of image quality, measured by CLIP-IQA and MUSIQ, approach is consistently competitive and often surpasses baselines across all control types. Notably, SIGMA-GEE performs strikingly well in the multi-subject setting, significantly outperforming all baselines across every evaluation metric.

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Iterative Insert-Anything'
SIGMA Gen
Run time (sec)
DINO-I
SigLIP-I
SigLIP-T
Depth MSE
CLIP-IQA
MUSIQ
200
200
40
40
00
Narbor 6.paac
Narbber sugeest
Nomber cutjpcts
Nambber oujeedt
Nainbor sudject
Narbber oubjecs
Numberr of sutjecs
Figure 5: Performance over increasing number subjects. Baseline Iterative Insert Anything* run-time increases and quality decreases steeply compared to SIGMA-GEN. Our method also out- performs consistently in subject consistency and depth MSE.
Subject DINO-I
Sigh.I-11
Text SigLIP-T
Depth MSE
CLIP-IQA MUSIQ
Quality
Type Depth Cuntrol
DIND4 SigLIP-I
Subject
Text SigL.IP-T
Depth
CLIP-IQA MUSIQ
Quality
Mask (BG)
74.17
77.52
17.63
40.10
71.25
72.27
Subject depths (loken))
77.82
17.73
Mask dapth (ms))
74.54
72.82
17.73
26.38
72.64
73.21
Full depth (tukens)
74.32
77.46
18.08
24.43
72.83
23.38
Mask + depth (FULL)
74.80
77.99
18.26
25.17
73.36
73.53
Full deptt (ConzcllNer)
74.10
76.38
17.56
24.42
72.79
73.31
Table 2: Ablation over increasing guidance. (BG) represents text prompts describing only the background, whereas (FUI.I) describes the whole scene. Removing depth reduces perfor- mance while providing full prompts that include subject names improves performance.
Table Ablation over depth control. Pro- viding depth beyond the subjects to our method leads better quality, depth aligmment, and text alignment. Providing the full depth using Con- trolNet leads to loss subject consistency, and text alignment.
For multi-subject personalization with precise masks and depth, plot LIGMA-GEN'S perfor- mance as the number of subjects increases (Figure 5) comparing against the strong baseline of Iterative Insert Anything*. As expected, inference runtime grows much more steeply for the base- line due to repeated insertions. This iterative process also leads to a reduction in image quality, as reflected by lower CLIP-IQA and MUSIQ scores. In contrast, SIGMA-GEN maintains quality regardless of the number of subjects.
In terms of subject consistency, measured by DINO-I and SigLIP-I, SIGMA-GEN experiences some degradation as the number of subjects increases largely due to dataset distribution effects (see Appendix)-yet still significantly outperforms the baseline. Text-to-image consistency scores remain similar between Iterative Insert Anything* and SIGMA-GEN, with our method achieving higher performance on average. Finally, our approach preserves spatial consistency across subjects, as shown by stable MSE values, while the baseline's 's performance degrades with more subjects.
We show qualitative comparison for multi-subject personalization with precise masks and depth in Figure 3 We show examples with three, five and seven subject insertion. For the baseline, with three and five subjects, we see loss of identity in the eagle and dove respectively. For seven subjects, along with loss of identity of the person also observe that some subjects do not get inserted such as the chalkboard and the last two tables. In contrast, SIGMA A-EN successfully follows all spatial and subject controls.
We qualitatively compare the coarser tasks of 2D and 3D bounding box control against the baseline MSDiffusion, which also relies on bounding boxes. We show case of seven subject insertion in Figure MSDiffusion fails to maintain correct position or identity of the subjects. In contrast, SIGMA- A-N follows spatial and identity controls for both types of control granularities. We show quantitative evaluation on DrcamBooth Ruiz et al. (2023) dataset, human evaluation, comparison with commercial models, and additional qualitative results in the Appendix.
5.2 ABLATIONS
In Table 2 probe the performance of our method for the precise mask and depth scenario by 1) removing depth entirely while maintaining precise masks and 2) by providing full scene description as input prompt. Note than in Table I we used background prompts for the precise mask and depth case (also second row of Table 2 for fair comparison to baselines, but here we use full prompts for ablation in the last row. We observe that including the full prompt improves scores across all
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Place these subjects with colorful Holi decorations
Place these subjects ancient battleground
Background
Masks
Identities
Output
Background
Masks
Identities
Output
Figure 6: Insertion using SIGMA-GEN, We show extendability one-shot single and multiple identity insertion given a background image based on bounding boxes.
identitymage
Identity image
Depatta
Oopohs
Place
Place subjects white background
Generated imargss
Oenerated Imagga
Figure 7: Reposing subjects. Through various poses supplied using depth, SIGMA-GEN can repose deformable subjects on different backgrounds.
dimensions compared to using only the background prompt. We also observe that removing depth but providing precise masks (first row) reduces performance as less information is provided.
Secondly, in Table B we probe various methods of providing depth control to our trained model. We observe that providing the full depth improves performance all aspects except for subject consis- tency where it decreases slightly. However, all scores consistently surpass the baselines. We perform an experiment where we omit depth by passing the structure control image 1" all zeros, but pass depth externally through pre-trained ControlNet. We observe degradation in subject consistency, text alignment and quality compared to providing the full depth via our structure control. We include additional ablations in the Appendix.
5.3 OTHER APPLICATIONS
Insertion. Although SIGMA-GEE has been trained for personalized image generation based on spatial/identity controls and prompt, we show that we can enable single and multi-subject one-shot insertion in Figure g For this task, we utilize blended diffiusion (Avrahami et al. (022) as a plug- and-play method preserve the background. During inference, we follow the same strategy as we do for personalized generation, except we use the noised latents of the reference background image to preserve We show single and multi-subject insertion, where we observe that our method able to harmonize the style of the subject to that of the background while maintaining correct shadows and lighting. It should be noted that the multi multi-subject insertion we show here not iterative and is achieved in single denoising loop.
Reposing. In Figure 7 we show control over a pose of an entity while maintaining its identity using depths of different poses. We show examples of deformable subjects whose identities across poses more challenging to capture.
We show more emergent properties of SIGMA-GEN in the Appendix including the capability to handle free-form masks, style change, and multiple granularity levels control same generation. Additionally, we include qualitative examples using real references, high number of subjects.

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6 CONCLUSION
We present SIGMA-GEN, a unified framework for controllable multi-subject, preeettt-preserving image generation. Our single model can follow spatial controls while preserving both the identities and arrangements of multiple subjects, To support this, we in- troduce large-scale synthetic data generation pipeline that produces SIGMA-SE127K, dataset with up to 10 subjects per image, which we use to train SIGMA GEN. Through extensive evalua- tion, we demonstrate that SIGMA-GEN consistently outperforms baselines, with especially strong gains in scenarios involving five more subjects. Finally, we showcase additional applications of SIGMA-GEN, like subject insertion and reposing.
fine-to-coarse granularities
ACKNOWLEDGEMENT
SM and OS were supported in part by the National Science Foundation under Grant #2329927.
REPRODUCIBILITY
We provide details of our method for synthetic data generation in $ 3.1 and in the Appendix. We also state the implementation details of our method trained on said dataset in $ 4.1 Our dataset, models and code will be open-sourced at.
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13


Reviews:
[{"review_id": "dRmU33xXnZ", "reviewer": "Reviewer_ETmR", "summary": "The method focuses on the problem of fine-grained control in text-image generation. Existing methods support the function of controlling the single-subject structure or identity, but cannot handle the multi-subject structure and consistent identity with the accurate spatial layout. The proposed SIGMA-GEN proposed several modules to tackle the balance between accuracy and efficiency in multi-subject generation.", "questions": "See weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "Strength:\n-\tThe paper is well-motivated with several technical challenges;\n-\tThe method proposed seems sound and correct.\n-\tThe new evaluation sub-benchmark is proposed with a pipeline for the dataset generation.", "weaknesses": "Weakness: \n-\tIt seems confusing that which exact module corresponds to tackle the problem of multi-subject structure and identity. It seems that the proposed modules are not unique to this specific challenge. \n-\tThe novelty of the pipeline is limited. It seems integration of existing modules into a pipeline. Please directly compare with existing baseline methods and show the novelty of each module.", "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": "O6crfAZkkW", "reviewer": "Reviewer_cKMR", "summary": "This paper presents SIGMA-GEN for multi-subject identity-preserving image generation with structural control at various granularities. The authors introduce SIGMA-SET27K, a synthetic dataset with 27k images containing up to 10 subjects, and demonstrate improvements over baselines especially in multi-subject scenarios.", "questions": "- Can you evaluate on real-world data with actual photographs as identity images to validate generalization beyond the synthetic training distribution? How does performance compare when the identity images and test scenarios come from real captures rather than model-generated content?\n\n- Please clarify the identity routing mechanism mathematically and show whether the three-stage curriculum is necessary with ablations. Why not compare to other recent multi-subject methods you cite like MultiBooth, and can you provide user studies validating your metrics?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- Jointly controlling multiple subject identities and spatial layout at various granularities (2D boxes to pixel-level depth) with a single model addresses a practical need in creative workflows.\n\n- Strong quantitative results with 31 points improvement in fidelity and 4x speedup for 5+ subjects, with consistent gains across metrics.\n\n- Well-designed automatic data pipeline and comprehensive ablations provide good insights into component contributions.\n\n- Demonstrated versatility through applications like insertion, reposing, and mixed-granularity control.", "weaknesses": "- Limited technical novelty since the core architecture directly adopts OminiControl's [1] unified attention mechanism without significant modification. The main contribution is dataset engineering rather than methodological innovation.\n\n- Training and evaluating entirely on synthetic data is problematic. You're essentially fitting to outputs from Flux Kontext [2] and other models [3,4], then testing on the same distribution. This doesn't demonstrate real-world generalization, and you should validate on existing benchmarks like DreamBooth [5] with real photographs. Errors from the generation pipeline also propagate into your model.\n\n- Critical technical details are underspecified. How does the model map routing mask intensities (10, 20, 30...) to identity image blocks - is this explicitly supervised or purely learned? The three-stage curriculum suggests fundamental scalability issues rather than unified learning. Why can't you train end-to-end?\n\n- Incomplete baseline comparisons. You cite MultiBooth [6] and other recent multi-subject methods but only compare against MSDiffusion [7]. Also adapting MSDiffusion to use filled masks instead of coordinate embeddings may unfairly disadvantage it. No user studies validate whether your automatic metrics correlate with human perception.\n\n- Training with only subject depths when Table 3 shows full depth improves results (SigLIP-T: 17.73→18.08) seems arbitrary. The bidirectional compositing for occlusions feels like a heuristic workaround rather than principled depth reasoning like InstanceDiffusion [8].\n\n### References\n\n[1] Tan et al., \"OminiControl: Minimal and Universal Control for Diffusion Transformer,\" arXiv 2024\n\n[2] BFLabs et al., \"Flux.1 Kontext: Flow Matching for In-Context Image Generation,\" arXiv 2025\n\n[3] Ren et al., \"Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks,\" arXiv 2024\n\n[4] Wang et al., \"MoGe-2: Accurate Monocular Geometry with Metric Scale,\" arXiv 2025\n\n[5] Ruiz et al., \"DreamBooth: Fine Tuning Text-to-Image Diffusion Models,\" CVPR 2023\n\n[6] Zhu et al., \"MultiBooth: Towards Generating All Your Concepts in an Image,\" AAAI 2025\n\n[7] Wang et al., \"MS-Diffusion: Multi-Subject Zero-Shot Image Personalization,\" arXiv 2024\n\n[8] Wang et al., \"InstanceDiffusion: Instance-Level Control for Image Generation,\" 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": {}}, {"review_id": "tDicaK7n5p", "reviewer": "Reviewer_UzUG", "summary": "The authors introduce SIGMA-GEN, a unified framework designed for generating images containing multiple, specific identities. This method is presented as the first to achieve single-pass, multi-subject generation that preserves identity while adhering to both structural and spatial constraints provided by the user. A significant feature is the model's flexibility, allowing user guidance at various levels of precision (from simple bounding boxes to detailed segmentation maps) within one model. To accomplish this, the authors also created a large-scale synthetic dataset, SIGMA-SET27K, providing rich identity and spatial information. The paper claims state-of-the-art performance in identity preservation, image quality, and speed.", "questions": "I am interested in the model's performance on more fine-grained, real-world customization tasks. Specifically, how effectively does this framework handle high-fidelity, instance-level customization, such as preserving the exact facial identity of a specific, real-world person provided by a user.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The primary strength is its reported ability to handle multi-subject identity preservation in a single pass while simultaneously respecting complex structural and spatial constraints. This addresses a major limitation in existing generative models.\n- The model offers significant practical utility by accepting a wide spectrum of user guidance—from coarse 2D/3D boxes to precise pixel-level maps—within a single, unified framework. This versatility makes it accessible for different use cases without needing specialized models.\n- The creation of the SIGMA-SET27K dataset is a valuable contribution in its own right. A large-scale synthetic dataset with comprehensive annotations for identity, structure, and spatial information is a key enabler for this type of complex, multi-modal training and will likely benefit the wider research community.", "weaknesses": "- Lack of Clarity in Methodology and Reporting: The paper's presentation is not as clear as it could be. Specifically, the pipeline for constructing the SIGMA-SET27K dataset is underspecified; critical details about the tools and processing steps used are omitted, making the dataset's creation difficult to reproduce or fully evaluate. \n- Insufficient Experimental Baselines: The experimental comparison, while showing strong results against some methods, is not comprehensive. To truly validate the \"state-of-the-art\" claim, the evaluation needs to be broadened to include more recent and relevant open-source methods. A comparative analysis against top-tier commercial models (such as those from Google, e.g., gemini-2.5-flash-image-preview, or JiMengAI) is also necessary to properly contextualize the model's performance.", "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": "A0CMI8rndy", "reviewer": "Reviewer_i8jB", "summary": "This paper presents SIGMA-GEN, a unified framework for multi-subject, identity-preserving image generation under flexible spatial and structural controls. The method allows users to specify both (a) subject identities using exemplar RGB images, and (b) structural arrangements through controls of varying granularity—from 2D bounding boxes to pixel-level depth maps and 3D layouts.", "questions": "1. How does the model behave when trained on real datasets with authentic multi-person scenes (e.g., MS-COCO or Visual Genome)?\n\n2. Can the routing–structure image representation generalize to video or temporal settings?\n\n3. Are there failure cases when subject identities overlap or occlude one another heavily?\n\n4. How does the method handle domain shift between synthetic and real identities?\n\n5. Could the authors release data-generation scripts separately from trained models for transparency?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. The paper elegantly bridges subject personalization and structural control, previously treated as separate problems.\n\n2. The routing–structure control representation is compact and adaptable to multiple input modalities (2D/3D/depth).\n\n3. The authors benchmark across single- and multi-subject settings, include runtime analyses, and evaluate both fidelity and identity preservation.\n\n4. SIGMA-SET27K is systematically generated with aligned modalities, providing a valuable resource for future research.\n\n5. The framework aligns well with creative workflows (scene layout, compositing, virtual try-on), making it broadly relevant beyond academic interest.", "weaknesses": "1. The synthetic dataset may not reflect real-world visual variability; generalization to real photos is untested.\n\n2. All quantitative metrics are automated (DINO, SigLIP, MUSIQ); perceptual user studies would strengthen the claims.\n\n3. Some baselines (e.g., Insert Anything*) are inference-level adaptations, not retrained for fairness—potentially favoring SIGMA-GEN.\n\n4. The paper ablates over control granularity but not over architecture (e.g., without bidirectional compositing or without routing tokens).", "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)