RoomEditor: High-Fidelity Furniture Synthesis with Parameter-Sharing U-Net
Authors: Zhenyi Lin
Xiaofan Ming
Qilong Wang
Dongwei Ren
Wangmeng Zuo
Qinghua Hu
Venue: NeurIPS 2025 poster ()
Keywords: Image Generation
Primary area: deep_learning

Abstract: Virtual furniture synthesis, a critical task in image composition, aims to seamlessly integrate reference objects into indoor scenes while preserving geometric coherence and visual realism. Despite its significant potential in home design applications, this field remains underexplored due to two major challenges: the absence of publicly available and ready-to-use benchmarks hinders reproducible research, and existing image composition methods fail to meet the stringent fidelity requirements for realistic furniture placement. To address these issues, we introduce RoomBench, a ready-to-use benchmark dataset for virtual furniture synthesis, comprising 7,298 training pairs and 895 testing samples across 27 furniture categories. Then, we propose RoomEditor, a simple yet effective image composition method that employs a parameter-sharing dual U-Net architecture, ensuring better feature consistency by sharing weights between dual branches. Technical analysis reveals that conventional dual-branch architectures generally suffer from inconsistent intermediate features due to independent processing of reference and background images. In contrast, RoomEditor enforces unified feature learning through shared parameters, thereby facilitating model optimization for robust geometric alignment and maintaining visual consistency. Experiments show our RoomEditor is superior to state-of-the-arts, while generalizing directly to diverse objects synthesis in unseen scenes without task-specific fine-tuning. 
Our dataset and code are available at https://github.com/stonecutter-21/roomeditor.

RoomEditor: High-Fidelity Furniture Synthesis with
Parameter-Sharing U-Net
Zhenyi Lin Tianjin University linzhenyi@t, jtjj. edu. en
Xiaofan Ming Tianjin University xi xiaof anming@t edu. edu. en
Qilong Wang Tianjin University qlwangōtju. edu. en
Dongwei Ren" Tianjin University rendwStju. edu cn
Wangmeng Zuo Harbin Institute of Technology wmzuo@hit. edu. cn
Qinghua Hu
Tianjin University huqinghua@t.ju ju. edu. cn General scene
Indoor scene
Figure 1: Furniture synthesis with RoomEditor integrates reference objects into environments with geometric coherence and visual fidelity. Moreover, RoomEditor exhibits remarkable generalization capabilities across wide range of unseen scenes and objects without task-specific fine-tuning
Abstract
Virtual furniture synthesis, critical task in image composition, aims to seamlessly integrate reference objects into indoor scenes while preserving geometric coherence and visual realism. Despite its significant potential in home design applications, this field remains underexplored due to two major challenges: the absence of publicly available and ready-to-use benchmarks hinders reproducible research, and existing image composition methods fail to meet the stringent fidelity requirements for realistic furniture placement. To address these issues, introduce RoomBench, a ready-to-use benchmark dataset for virtual furniture synthesis, comprising 7,298 training pairs and 895 testing samples across 27 furniture categories. Then, we pro- pose RoomEditor, a simple yet effective image composition method that employs parameter-shane sharing dual U-Net architecture, ensuring better feature consistency by sharing weights between dual branches. Technical analysis reveals that conven- tional dual-branch architectures generally suffer from inconsistent intermediate features due to independent processing of reference and background images. In contrast, RoomEditor enforces unified feature learning through shared parame-
"Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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ters, thereby facilitating model optimization for robust geometric alignment and maintaining visual consistency. Experiments show our RoomEditor is superior state-of-the-arts, while generalizing directly to diverse objects synthesis in unseen scenes without task-specific fine-tuning. Our dataset and code are available at http:://fftuubcoom///mmccteeeetr
Introduction
Recent advances in augmented reality and computer vision have revolutionized virtual product visualization for e-commerce applications, particularly in indoor scene synthesis. Within the rapidly expanding home design market (expected to reach around 250 billion dollars in ten years [15), intelligent image synthesis systems have emerged as critical tools for consumer decision-making These systems enable in-situ furniture visualization by digitally integrating selected items into user-provided room images as shown in Figure I, offering significant potential to transform design practices and retail experiences.
Despite progress in generic image composition, indoor furniture synthesis remains underdeveloped due to two key limitations. The first lies in the limited availability of ready-to-use datasets with realistic indoor environments, thereby hindering reproducible research. While Amazon's dataset demonstrates the value of domain-specific data, its restricted access stifles community efforts. InteriorNet [30), 3D-FUTURE [14), and 3D-FRONT [13] expose detailed 3D models, but constructing pairs demands meticulous viewpoint selection and scene setup, revealing challenges in data acquisition. Consequently, the field remains hampered by inadequate benchmarks and persistent data scareity. To address this issue, we introduce the RoomBench dataset, ready-to-use, publicly available benchmark tailored for virtual furniture synthesis. Our RoomBench consists of 7,298 training pairs and 895 testing samples across 27 furniture categories, meticulously curated from diverse online sources. Each sample includes high-quality annotations, enabling comprehensive evaluation of geometric coherence and visual realism. This benchmark provides a foundation for future research in indoor furniture synthesis by facilitating both training and evaluation.
reasonable eeference-backrroune
The second critical challenge arises from technical limitations in current image composition meth- ods [53 6 7. which often fall short of the high visual fidelity required in practical home design applications. Minor geometric misalignments or texture inconsistencies can break user immersion and erode trust. Image encoder-based methods such PBE (53) and Anydoor (6) use CLIP (38 or DINO 3 to extract reference features and inject them into the U-Net. However, they tend to overlook Ane-grained structural details essential for complex furniture integration. Even advanced dual U-Net architectures like MimicBrush 7 struggle to ensure visual consistency and harmony between the inserted object and the surrounding background, often resulting in unnatural compo- sitions with obvious copy-paste artifacts. These limitations result in inconsistent appearances and poses of furniture, thereby undermining practical utility. To address these limitations, we propose RoomEditor, a parameter-sharing dual U-Net that enforces feature consistency via shared weights. We perform an in-depth analysis of existing dual U-Net pipelines and identify feature misalignment between the extraction and inpainting stages, which degrades synthesis quality. By unifying feature extraction and inpainting within the same network, RoomEditor naturally aligns the two stages, enabling complex spatial transformations and seamless object integration. Experiments demonstrate that our method achieves superior objective and subjective results on RoomBench compared to state-of-the-an methods.
Our RoomEditor not only excels in home design but also generalizes effectively a broader range of scenes and objects. As illustrated in Figure 1 our RoomEditor can be directly applied to general scenes and objects without any task-specific fine-tuning. The superior adaptability of RoomEditor can be attributed to its unified feature learning in parameter-shared U-Net. The contributions of this work are summarized follows:
We constructed ready-to-us open-source benchmark RoomBench specifically for home design, consisting of 7,298 furniture- baattrre-b training pairs and 895 testing samples across 27 categories with annotations.
We propose a simple yet effective RoomEditor for high-fidelity furniture synthesis, whose core is a parameter-sharing U-Net. In addition, we provide an in-depth analysis of existing

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dual-branch approaches from the perspective of feature consistency, showing the potential advantage of RoomEditor for high- fide-ficlityy and visual consistency.
Extensive experiments validate that RoomEditor achieves superior performance in both quantitative and qualitative evaluations as well as human perception studies for home design, while demonstrating remarkable generalization across diverse scenes and objects.
2 Related Work
2.1 Furniture Synthesis for Home Design
The most relevant research field to furniture synthesis is virtual try-on [8 8 28 26 50, 60), which has primarily focused on fashion applications, emphasizing fabric textures, body pose estimation, and occlusion handling. While several virtual try-on methods [26 50 60) employ dual U-Net architectures and must model garment-body interactions, their objectives and challenges differ from those in furniture synthesis. In particular, furniture synthesis requires handling diverse and rigid object shapes, whereas clothing in virtual try-on typically exhibits more consistent forms, leading to different integration challenges. Mcanwhile, the area of indoor scene synthesis specifically for furniture placement remains relatively underexplored. A few studies through text-to-image generation but provide limited control over individual furniture placement. to-Chooe [44] addresses furniture synthesis by incorporating reference features via a U-Net encoder augmented with FiLM (37) layers large-scale indoor dataset. However, its training and testing sets are proprietary, limiting reproducibility and further research. To bridge these gaps, this paper introduces RoomBench, a ready-to-use and open-source benchmark for furniture synthesis, along with a method RoomEditor that ensures high-fidelity object preservation and context-aware placement.
have explored interior design
2.2 Image Composition
primarily focused on the task of image harmonization, ensuring seamless integration of the foreground with the background. These methods typically relied on manually designed pipelines. In contrast, text-to-image diffusion models [40 have enabled the automatic integration of specific objects into diverse contexts while preserving their identity and attributes. By extracting pscudo- words, methods such as Textual Inversion [16] and DrcamBooth [41], along with others [2 19 24 25
Early image composition methods 23
9 45
46
47 10)
39
22
31 33 29 (7), rely fine- tuning with textual prompts but often produce unstable backgrounds. Semantic image composition methods, such DreamPaint 43 PBE [53], CustomNet (54). and ControlCom (53. which focus on object insertion into predefined scenes, often struggle to preserve fine-grained details in complex scenes when using CLIP (38) image encoder. Recent advancements, including AnyDoor (6) have addressed some of these challenges by leveraging ControlNet (57) and DINO (35] to improve texture fidelity and reference-object consistency. In contrast, MimieBrush 7 employs dual U-Net 22 (2), which has proven effective in capturing multi-scale reference features, thereby enabling flexible image editing and generating high-fidelity syntheses. Despite these improvements, achieving both high fidelity and seamless integration in home design remains a significant challenge. particularly when the training dataset insufficient.
architecture [51] |56] 59 5
3 Benchmark for Furniture Synthesis
In this section, we construct an open-source RoomBench for furniture synthesis, whose procedures for data collection, filtering, and annotation are detailed as follows.
3.1 Data Collection and Filtering
To ensure the diversity in furniture types and scene contexts, we systematically collected about 4,500 furniture items, each of which has several product images and contextual scene backgrounds. Our curation strategy included two key measures. Category Coverage: Hierarchical classification into 27 fine-grained categories, including bedroom essentials (e.g.. Bed, Pillow), living room furnishings
(e.g., Sofa, Coffee Table), and bathroom fixtures (e.g., Shower, Bathtub). Image Quality Assurance:
We require a minimum resolution of 512 x 512 for both furniture images and background images.
3

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Refocme Festares
ReNet
Unateertinn Pramltinnue
Backround Featares
Fool Badigecud Fozures
Background
ReNex
The architecture of our RoomEditor. Our method shares parameters between the two U Nets for unified feature space learning. As shown, reference features propagate independently. while background features interact with reference features through self attention module at each layer, ensuring effective feature alignment.
Figure 2:
Subsequently, the collected data underwent rigorous filtering. Initially, we manually classified images as either product images or background images. We then leveraged GPT-4o [ to assist in data filtering. Images were excluded the furmiture was fully visible in the background image there was inconsistency between the furniture object or color in the reference and background images (i.e., they did not depict the same object), resulting 5,948 reference furniture images and 4,989 background images. Following this, the dataset was split into training and testing sets. The training set comprises 5,288 reference furniture images and 4,094 background images, with each reference image paired with one or two background images, resulting in total of 7,298 cumnitur-bbacgrrunn pairs. The testing set contains 660 reference furniture images and 895 background images, resulting in 895 testing samples. For more detailed data-prost protesssoessing procedures and complete dataset information, please refer to Appendix B
3.2
Data Annotation
For each image pair RoomBench, background image and reference image are annotated with corresponding furniture masks. In the training set, these masks are hand-labeled precisely to ensure high-quality annotations. This labeling process allows effective data augmentation, improving the model's 's generalization capabilities. For the testing set, we included more loosely annotated masks to reflect variability and potential inaccuracies in user-generated labels. This annotation strategy allows us to evaluate the model's robustness in real-world scenarios, where annotations are often imperfect or inconsistent. With specific images and annotations, RoomBench is immediately ready- to-use, climinating the need for additional efforts in data acquisition that are typically required by InterionNet [30], 3D-FUTURE [4], and 3D-FRONT [3].
RoomEditor for Furniture Synthesis
In this section, we first introduce the RoomEditor architecture, whose core is a shared U-Net model for background and reference images with unidirectional information flow. Then, we compare our RoomEditor with existing methods from the perspective of feature consistency, showing the underlying mechanism behind our RoomEditor.
4.1 RoomEditor Architecture
Given masked background image 1 and masked reference image T. our goal is seamlessly integrate the object from M into the masked region I Different from existing dual U-Net methods (e.g., 7), our approach utilizes a shared U-Net to synchronously extract reference image features and perform inpainting- Specifically, as shown in Figure 2 we employ an inpainting U-Net <o(-) as basic model by following previous work [7]. First, we extract the features for IM from the mid- and upper-attention layers of the U-Net. For /-th self-attention layer, the extracted feature of

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I denoted as fi(I M) is computed as
Qre KJ
fi(I = softmax
Vin
(I)
dd
where Que -i WQ. fi WK. Vit = (I ) Wv, and W (Q.K.V) are the projection matrices in attention layer. fi-1 M indicates the output of the (1-1)-th layer. is worth note that our RoomEditor actually takes the image, mask and noise as inputs, while we omit the mask and noise in our formulation for simplicity.
Then, RoomEditor performs a unidirectional interaction to inject features of reference image Jt-1 (TA into those of masked background image bog for inpainting. Particularly, the features of I M in 1-th layer is computed by mixture attention with inputs of fi and fi i.e.
Qbe
K (og.reff
fi( softmax
Viggref)
(2)
vdg
where Qng fi-1 We: K [bgg,of]
cat(Ji- Si-1 (f) Vog.ed]
bg Wy and cat indicates the concatenation operation. Besides, existing
cat(fi-1 ( (I. dual U-Net methods 50 generally integrate reference features by using extra image encoders (e.g..
fi-n fi
CLIP (38)). Due similar functions achieved by mixture attention in Eqn. (2). our RoomEditor can omit extra image encoders to make our architecture more succinctly.
For optimizing our RoomEditor, we first construct masked background as 1og = Mog @ Let by giving ground-truth image gg and mask Mbg. and then sample random noise and a timestep to generate the noisy image gge Finally, we minimize the following loss function, i.e.,
L == Br. 1o || (T Tog C, () <
where с denotes the features extracted from 1
4.2 Discussion on Merit of RoomEditor
In this subsection, we compare with previous works 53 6 7 from the perspective of feature consistency, and show the potential advantage our RoomEditor architecture the context of high-fidelity furniture synthesis task.
4.2.1 Task Description
Since high-fidelity furniture synthesis aims to prominently place reference furniture Lef in the background image Jog. it can be formulated as an ideal copy-paste problem. Specifically, the target image Ig can be represented as composition of masked background image Mag 2 Jog and masked reference image Mit O Jeet with pose transformation R. i.c., Ig = Mng @ Iog + R(M @ Iat). where indicates element-wise multiplication. Therefore, the key issue is learning the precise transformation R. given a pair of background and reference images [Tng. Jee) As RR depends on the spatial and visual relationship between Ing and I including factors such mask geometry and background illumination, the final objective can be formulated as
I M @ Dog + R(M @ Let Mog @ Ibg).
(4)
To learn transformation R, diffusion model eØ (-) is generally employed to achieve this implicitly by training on abundant pairs of background and reference images {Tog I with masks {Mag Mar)
Based on the empirical observation that inpainting models implicitly preserve unmasked regions during training, treat predictions in these areas ground truth. Accordingly, consider two complementary cases: (1) masking the object region in Ig to obtain IM where the background prediction remains unchanged; and (2) masking the background region to retain only the object (denoted as R( D)), where the object prediction remains unchanged. Combining these two masked predictions yields an approximation of the target image:
(0(Ig) = (Mng 4 Tng + Mbg c R (IM
b
(5)
Mog E0 (IM) + Mog E0 (R (IM IM)
5

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where bog and tef represent Myg @ Jog and Met 3 Let, respectively, and Mog = Mog denotes the complement of the mask Mog.
Similarly, since the inpainting model's output in non-masked regions remains essentially unchanged regardless of input conditions, its impact at the feature level is marginal. Thus, for the (-th layer, we obtain
g(( - ft (Mog 2 Ing + Mog @ R. (
bgg
(6)
Mog St (T Mog Ji (R
Furthermore, the diffusion model implicitly learns R. by progressively transforming and fusing features each layer, rather than by directly transforming the original image. Thus, Eqn. (6) can be rewritten as follows in practice:
fi (Lg) & Mog fi (1 Mog R4 (1 fi (1
(7)
where R, denotes the transformation in feature space. Given that the first term solely related to the background, the differences among various methods [53 6 7 are primarily concentrated on the second term. According to Eqn. (6) and Eqn. (5). better feature consistency between ground-truth
gi( and practice one (i.e., the right part of Eqn. (6)) will help to optimize the inpainting model
in Eqn. 5
The experimental verification of Eqn. ( and Eqn.
(6) is presented in subsecction C.I
4.2.2 Comparison with Previous Works
From the perspective of feature consistency, we compare our RoomEditor with several previous works [53 6 77. Here we consider the I-th interaction layer and show how different methods frozen image encoder
approximate fi)) Encoder-based methods [53 6) take advantage
F(()) and a trainable linear projector T..-) to extract reference features, formulated as Er.
M( Then, cross-attention is applied to inject the reference features L(E E R xd into the background ones:
gt Cross-Att((f- EL (Tg)).
(8)
where Cross-Autn(s)) denotes cross-attention.
Dual U-Net methods (e.g.. (7)) employ frozen U-Net (-) extract attention features at (l-1)
-th layer. These features are injected via mixture attention (which can be approximated as a blend of self- and atteentttenionn.. expressed as
fi(La) (elf-Att((fi-(AF))) Cross-Att(ff- 9- (I
f:(1))
Apran (gt (1 Wy
where Self-Att(:;-) denotes self-attention and A (pgret denotes attention
matrix. Compared to Eqn. 7.. these methods approximate R, (f f((I through the operation Aprgret| (gt-1 (1 Wy, while Mag and Mog are implicitly learned through the attention mechanism.
In contrast, our RoomEditor uses single U-Net fi-1(-) to extract reference features which are integrated through mixture attention:
fi (Lg) ~ Self-Attn( (t- Cross- (ttn(fi- fi (MM
(10)
f((
Abgar (ft- (LM)) Wv
Compared to Eqn. 77. our method approximates (((f((TM) fi (((TM) using A A (fi- Wv. Under this formulation, we assume R₂ corresponds to a cross-attention operation, which applies a complex transformation the reference features based on their similarity to the background features. It is worth note that the optimal (²(-) may be not fi(.), but f((-) is a natural choice for offering the promising performance. Empirical comparisons in terms of loss visualizations and image quality indicate that the shared U-Net outperforms both the frozen U-Net and the unshared one.
Empirical Validation. To further investigate the effect of feature consistency, we compare three different methods on our RoomBench, i.e., frozen reference U-Net as in Mimicbrush [7]. trainable reference U-Net for reference images, and our RoomEditor with shared U-Net for background and reference images. Specifically, we train all models RoomBench under the same exactly setting,

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Lapero
Laptrc
0.10
0.4
Reffrance
Berference
3.55
nenterence (tral)))
0.25-
3.00
411
Retterence (trine))
Berfewenn dhrste))
0.02-
Fetfeeence Oirones
Rnaterencc (baln)) Diaann Sauria
The Aecorrnn Bhait
Padierence Oruin)
(ours)
Jounal
2.44
607-
0.0
2006
4000
0300
0003
2000
13300
Figure 3: &g error values in different layers by three methods: our RoomEditor (green line), and two non-shared dual U-Net methods with frozen (blue line) and trainable (red line) reference U-Net.
and compute & loss between features from two separate inputs (Ti(Tz:) and those of the ground-truth images, which are outputted by the intermediate layers of inpainting U-Net (f)(Tg)). Figure shows the results of different methods along with various layers and training epochs, where we can observe (I) our RoomEditor with shared U-Net (green line) is consistently low in E2 loss across all intermediate layers. Particularly, RoomEditor is significantly lower than other two methods at the early stages in terms of &2 loss. (2) The frozen (blue line) and trainable (red line) reference U-Net only approximate the features of ground-truth images at the last stage under the strong constraint of training loss. (3) The frozen reference U-Net has lower & loss than trainable one in the early stages, indicating unshared U-Net is more difficult to optimize, due to large parameter space and limited training data. The above comparisons show that our RoomEditor, using shared U-Net, achieves better feature consistency, leading to better performance (refer to experiments Table 4).
Experiments
5.1 Experiment Setup
Implementation Details. Our RoomEditor is initialized with the Stable Diffusion 1.5 inpainting model [40] and trained on the RoomBench dataset for 20k steps with batch size of 32 across four NVIDIA A6000 GPUs. We use the Adam W optimizer 32 with a constant learning rate of 1 x 10-5 and set the input resolution 512 x 512. To enhance robustness against imperfect mask inputs and domain variations, we apply augmentation techniques to both masks and images. The augmented masks are shown in Figure
Objective Evaluation Metrics. To compare with other methods, we use SSIM 48] and PSNR 21 for assessing reconstruction quality, FID (20] and LPIPS (58) for assessing perceptual realism,
CLIP-score 38 and DINO-score 35 for assessing semantic consistency.
Human Evaluation Metrics. To complement objective metrics, conducted a user study follow- ing 77) evaluating results in fidelity (retaining reference identity and details), harmony (seamless integration with the background), and quality (overall visual appeal and detail).
5.2 Evaluation on RoomBench
Anydoor 66 and Mimicbrush (7) These competing methods are evaluated under three settings: the released models trained on their respective large datasets, the fine-tuned models (denoted with x), whose released model weights are further fine-tuned
Our RoomEditor is compared with PBE 53
7

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Train Data
Method
RoomBeench
FID4
SSIMT
PSNR1
LPIPS
CLIPT
PBE
533
1.4M
X
28.35
0.766
19.96
0.139
84.63
75.72
AnyDour d
500K
X
28.03
0.767
18.85
0.137
87.74
76.91
MimicBrush
400K
X
22.50
0.784
19.41
0.111
86.21
79.11
PBE
0.136
AnyDooe"
500K
/
26.62
0.772
19.13
0.135
88.73
79.37
MimicBrush
400K
/
19.04
0.791
20.44
0.0%%
90.87
85.65
MimicBrush'
21.12
0.785
20.39
0.106
89.48
83.26
RoomFditor (Ours)
18.42
0.793
21.15
0.094
90.51
85.47
Table I:
Quantitative comparison on RoomBench. PBE, AnyDoor, and MimicBrush are evaluated under three settings: (i) released models on large datasets, (ii) fine-tuned on RoomBench (marked with +), and (iii) re-trained on RoomBench (marked with f). f: data volume after filtering via SD [40].
Fidelity Best (%)
Fidelity Rank]
Harmony Best (36
Harmony Rankj
Quality Best (%)
Quality Rank
Method
PBE 53
5.1
3.36
18.0
2.85
4.5
3.34
AnyDoor
8.9
2.96
7.6
3,16
3.5
3.20
MimicBrush
29.4
2.11
30.5
2,16
38.3
1.80
RoomEditor (Ours)
56.6
1.57
43.9
1.82
53.6
1.66
Table 2:
User study results. each trial, annotators were presented with four images (one from each method) and asked to rank them with respect to fidelity, harmony, and overall quality. "Best (%)" denotes the percentage cases where method was ranked first (i.e., perceived as the best under given criterion), while "Rank!" indicates the average ranking (lower is better) across all trials.
on our RoomBench, and the re-trained models (denoted with t), whose model parameters are trained by keeping the same configuration as our RoomEditor.
5.2.1 Quantitative Comparison
As shown in Table [ the released models of PBE, AnyDoor, and MimicBrush exhibit limited performance for furniture synthesis despite being trained on large-scale datasets. Notably, the large- scale datasets used for training AnyDoor and MimicBrush primarily consist of video data. The number of training image pairs is estimated be approximately four times the number of videos, significantly exceeding the scale of our RoomBench dataset. This highlights the necessity of establishing a specific dataset tailored for home design. Fine-tuning on RoomBench significantly improves the performance of AnyDoor" and MimicBrush* across all metrics. In particular, MimicBrush" achieves the highest CL.IP-coore and DINO-score.
From a technical standpoint, our RoomEditor, which trained solely on RoomBench, outperforms competing methods in most quantitative metrics. Specifically, the state-of-the-art MimicBrushl notably inferior to both MimicBrush" and our RoomEditor, demonstrating that our proposed parameter-sharing U-Net architecture is a more effective solution for image composition, especially when scale pre- training datasets are not available.
5.2.2 User Study
Following the protocol in 77. we conduct user study to evaluate the perceptual quality of generated images. We let 25 annotators (14 undergraduate students, parents of some of the students, and university faculty or staff members, all informed of the evaluation criteria) rank one hundred randomly selected generation results of different methods based on our benchmark (introduced sesceetnnnn 3) from three aspects: fidelity, harmony, and overall quality. As shown in Table 2] our RoomEditor outperforms competing methods across all aspects. Notably, while MimicBrush" achieves superior results some objective metrics, it lags behind our approach in real user assessments.

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Masked Background Refecence
Image
PBE
Anydoor
MumicHrush
Ous
Figure 4: Visual results on RoomBench. PBE and AnyDoor achieve good background harmony but lack object consistency. MimicBrush preserves fidelity but suffers from a copy-paste effect. In contrast, our method ensures both high fidelity and strong background harmony, resulting in realistic and coherent furniture synthesis.
Method
FID1
SSIMf
PSNR
LPIPS
CLIP
DINO1
PBE 53
108.57
0,580
15.98
0.340
80.98
69.98
AnyDour
5
86.07
0.567
14.51
0.372
86.41
82.67
MimicBrush
?
77.16
0.585
15.35
0.361
87.66
83.70
RoomFditor (Ours)
68.78
0.594
16.43
0.304
89.62
85.04
Table 3: Quantitative evaluation on the DreamBooth dataset. We compare our method with existing approaches. Despite being trained on only on RoomBench, our method achieves the best results, showeasing strong generalization capability.
5.2.3 Qualitative Comparison
As illustrated in Figure 4 PBE [33 fails to keep object identity. leading significant deviation from the reference. AnyDoor 6 better preserves identity but faces issues of improper scaling, color mismatches, and misplacement. MimicBrush (7) maintains object structure but suffers from artifacts like copy-pase paste effects and distortion. In contrast, MimicBrush* and our RoomEditor achieve seamless integration, realism, and correct orientation. Given the large-scale dataset used for pre-training MimicBrush", our RoomEditor is more suitable for furniture synthesis.
5.3 Generalization Diverse Scenes and Objects
We evaluate the generalization performance of our model using the DreamBooth dataset [41] which contains 30 object categories, including backpacks, animals, sunglasses, and cartoon characters. Each sample is annotated with object masks, consistent with the RoomBench dataset. In this experiment, the RoomEditor model neither re-trained nor fine-tuned on external datasets. We compare with publicly available models from [53] 6 77. As shown in Table 3 our method significantly
outperforms existing approaches trained on large-scale datasets. These results demonstrate strong generalization ability of RoomEditor in synthesizing realistic object placements in unseen environments, indicating its robustness for real-world applications. Qualitative results in Figure 12 further show that RoomEditor naturally integrates diverse objects and scenes.

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All / Train
Method
FID.
SSIMT
PSNRT
LPIPS.
CLIPT
DINOT
(M)
RoomEditor (Ours)
943
815
18.42
0.793
21.15
0.094
90.51
85.47
RoomEditor+CLiP
1803
860
18.38
0.792
21.15
0.094
90.45
85.55
Dual U-Net (Frozen g)-CLIP
21.12
0.785
20.39
0.105
89.48
83.26
Dual U-Net (Trainable 9)+CLIP
2432
1717
19.69
0.787
20.56
0.099
90.09
84.36
Table 4: Ablation Study. We assess the impact of incorporating CLIP and varying dual U-Net configurations. Adding CLIP improves semantic alignment. Making the reference branch trainable improves fidelity but increases parameters significantly.
5.4 Ablation Study
Since our RoomEditor is highly concise, we conduct ablation study to evaluate its integration with key techniques (e.g., CLIP image encoder and non-shared reference UNet g) in existing methods as shown in Tabable 4 First, we examine the integration of CLIP image encoder, which is commonly adopted to facilitate feature extraction from reference images as in MimicBrush, resulting in RoomEditor+CLPP Although incorporating CLIP leads to a slight improvement in certain metrics, it introduces substantial increase in the number parameters. Next, we analyze the effects of non-shared dual U-Net configurations, where the reference U-Net g is either frozen or trainable. The results show that without our parameter-sharme
strategy, both variants exhibit significant performance degradation, particularly when the reference U-Net is frozen. We note that the variant with frozen g is exactly the same as MimicBrush These findings verify the effectiveness of our parameter-sharing U-Net design for unified feature learning.
6 Conclusion
This work advanced furniture synthesis by addressing two key challenges: absence of ready-to-us benchmarks and feature space divergence in dual-branch architectures. Specifically, we collect and release RoomBench, a ready-to-use benchmark for furniture synthesis, while presenting RoomEditor, a parameter-sharing dual U-Net architecture that ensures robust feature alignment. By training our RoomEditor on the collected RoomBench, it achieves state-of-the-an performance for furniture synthesis in terms geometric coherence and visual fidelity and demonstrates strong generalization ability diverse scenes. We hope that our work can encourage further research in furniture synthesis.
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (62576241, 62276186, 62172127, and U22B2035).
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A Implementation Details.
Hyperparameters. our experiments, the inpainting U-Net initialized with the Stable Diffusion 1.5 [4] inpainting model, which takes 9-channel input, while the reference U-Net is initialized with the standard Stable Diffusion 1.5 [40] model, which takes a 4-channel input. For the CLIP (38) model, employ CLIP-H the image encoder, following [7]. During both training and inference, images are processed to a resolution of 512 x 512 by first padding them to a square aspect ratio and then resizing them accordingly. To compute the CLIP-score and DINO-score,
CLIP VIT-B/32 [88 and DINO VIT-S/16 [31, following the protocols of [1 53 7 For the user study, we recruited 20 annotators to evaluate and rank different methods based fidelity, harmony, and overall quality.
Data Augmentation To enhance robustness against imperfect masks and domain variations, apply both image and mask augmentation, as shown in Table
Type
Augmeutation
Description and Parameters
Horizontal F Flipping
Applicd with probability of 50%.
Rotation
Applicd with probability of 50%, 30".
Image
Scaling
Applied 30% probahility, range +20%.
Cropping
Minimum retained ratio: 0.75.
Pertorbation
probability: Random dilation and crosion variation.
Blurring
25% probability; Boundary smoothing via blurring and thresholding.
Mask
Blox
25% probubilit; Mask replaced with its hounding box. probability: Original fine-grained retained.
No Angmentation
Table 5: Image and Mask Augmentation Strategies
B RoomBench Dataset
In this section, we introduce the construction and characteristics of our RoomBench dataset. As summarized in Table 6, RoomBench covers a broad range of furniture categories with diverse styles and high-quality images, enabling realistic simulation of real-world indoor scenes.
To construct the dataset, we first collected raw images from the internet. The data filtering process, illustrated in Figure 5) was conducted with the assistance of GPT-4o [ which helped to efficiently filter and organize high-quality image-text pairs. For the training set, we performed precise, pixel- level mask annotation, which supports various mask augmentations eerappllttinnsseeplllls of which are shown in Figure 6) contrast, the testing set was annotated using coarse masks to better reflect real- world conditions, where user-pro eser-provided annotations are often noisy or imprecise, as depicted in Figuue This annotation strategy allows us to evaluate model robustness under imperfect labeling scenarios.
Instruction Prompt
You are a professional multimud- cleaning assistant, responsi. ble for processing furniture image data sets. Your task is to determine whether a given product image co- impletely appears the correspond- ing hackground image. Please ans- wer "yes" or "no".
Filter
GPT-4o
Figure 5: Diagram of data filtering, where images first are labeled furniture and background images. Then, GPT-4o with specific prompt is used to filter out unmatched and incomplete images.

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Categories
Training
Testing
Sofa
1294
61
Lamp
1280
231
Coffee Table
24
Wandrobe
681
13
Armehair
563
61
Bed
460
9
Pillow
440
73
Nightstand
319
36
Desk and Chair
158
118
Other
1298
269
Total
7298
895
Table 6: Categories and distribution in RoomBench.
Figure 6: Different forms of data augmentation for precisely annotated mask in training set.
(a) Training samples
(b) Testing samples
Figure 7: In RoomBench, precise masks annotated for the training samples and can be augmented accordingly. Coarse masks are provided for the testing samples to simulate user interactions.
C Additional Experiments
C.1 Additional Verification Experiments for Eqn. (5) and Eqn. (6)
From subsubsection 4.2.1 we derived Eqn. 5 and Eqn. (6) based on the empirical properties of inpainting models and assumptions about the feature space. To more rigorously justify the validity of these assumptions and properties, conducted the following experiments.

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Training Loss (Avg.)
Region Merging Loss (Avg.)
3.9
10-2
4.3 10
Table 7: Quantitative comparison between the average training loss and the region merging loss.
Layer
d0
d3
45
m0
ul
u1
u4
u.
u7
u8
cos_sim
0,962
0.955
0.942
0.931
0,922
0.917
0.912
0.904
0.906
0.913
0.921
0.925
0,927
0.937
0.941
0.945
Table 8: Cosine similarity across feature layers. Here, d, and u correspond to the downsampling, middle, and upsampling blocks of the network, respectively.
For Eqn. (5) the core assumption is that the inpainting diffusion model can preserve the unmasked regions of image with high fidelity. Therefore, our goal is to demonstrate that the expression
Mag @ co( (Tg) + Mog @ co (R (
can effectively approximate the ground truth (GT) denoising prediction (g(tt)) To verify this as- sumption, we conducted experiments using pretrained diffusion inpainting model on our full test set. During the experiments, we added random noise the input images and sampled random timesteps t. The model was then provided with partial content from the full image (either the background or the object regions). We extracted the predicted noise components from the corresponding regions and computed the La loss as the evaluation metric:
[Et( - (Mog O ea ( Mbg O ( (
The results summarized in Table 7 show that the merged result-obtaine by feeding the model with separated regions and combining their corresponding outputs--icld a significantly lower loss than the average training loss (i.e., the average denoising loss during model training, visualized in Figure 3), thereby supporting the validity of the assumption in Eqn. (5).
For Eqn. (6) the underlying assumption is that property analogous to that in Eqn. (5) also holds the feature level. To evaluate this hypothesis from a similarity perspective, we adopt cosine similarity the evaluation metric. Specifically, we compute the cosine similarity between the feature representations extracted from different attention layers on the validation set, defined as
cos_ cos_sim sos_sim siimfil( (((g)) Mog @ f((I + Mog O f(( I)))
where fr(-) denotes the feature map extracted from the 1-th layer of the model. The results in Table g show that cosine similarities remain consistently above 0.9 across most layers, supporting the validity of the assumption in Eqn. (6) and indicating that the feature-level composition effectively preserves the representational coherence of the full image.
C.2 Generalization to Unseen Datasets: 3D-FUTURE Results
To evaluate the generalization ability of our method on unseen datasets, we conducted cross-dataset experiments using the 3D-FUTURE [14] dataset, which differs from our RoomBench in visual domain and object composition.
We selected 1,020 samples covering 34 furniture categories (randomly 30 samples per category). As the dataset does not provide paired reference images, we generated pseudo-pairs by horizontally flipping reference objects and applying Gaussian-blurred masks to prevent trivial copy-paste solutions.
As shown in Table 9 despite being trained exclusively on RoomBench, our model achieves compara- ble or superior performance to MimicBrush [7] on most metrics, demonstrating strong generalization to unseen furniture data and novel datasets without fine-tuning. The visualized results are shown Figure
C.3 Comparison with GPT-4o
To further benchmark our method against powerful commercial system, we include the closed-source GPT-4o (34] in our evaluation. As state-of-the-an multimodal model capable of handling multi-

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Method
FID1
SSIM
PSNR
LPIPS
CLIPT
DINOT
MimicBrush f
14.20
0.6595
20.64
0.272
80.10
64.61
RoomEditor (Ours)
13.91
0.6581
20.93
0.260
80.28
66.97
Table 9: Cross-dataset evaluation on 3D-FUTURE 14]
Our model outperforms MimicBrush across multiple metries, demonstrating strong generalization unseen data distributions.
Figure Additional visualization results the 3D-FUTURE
44
dataset.
Model
FID (1)
SSIM (t)
PSNR (f)
LPIPS (L)
CLIP (1)
DINO (t)
MimicBrush 7
56.44
0.780
19.40
0.113
88.12
79.49
GPT-4o +
72.12
0.502
13.84
0.290
87.80
83.30
RoomEditor (Ours)
44.78
0.792
21.05
0.094
90.42
85.12
Table 10: Quantitative comparison with GPT-4o
on a 200-sample subset RoomBench.
image inputs, GPT-4o exhibits strong general-purpose image editing and composition capabilities, making it valuable reference for assessing high-level visual reasoning and semantic understanding.
For fair and efficient comparison, we evaluate GPT-4o 34] MimicBrush 77). and our method on randomly sampled subset of 200 images from the RoomBench dataset. This subset size ensures sufficient scene diversity while keeping closed-souree inference costs tractable. comprehensive set of quantitative metrics is used to capture complementary aspects of visual fidelity and semantic consistency, including FID, SSIM, PSNR, LPIPS, CLIP, and DINO. Among these, CLIP and DINO are particularly informative for assessing object-level fidelity of the key challenges in compositional image editing.
The quantitative results are summarized in Table 10 Metrics that emphasize fine-grained spatial and structural consistency, such as FID and LPIPS, show GPT-4o performing notably worse than both MimicBrush and method. In contrast, its CLIP and DINO scores comparable to higher than those of MimicBrush, reflecting strong global semantic coherence and visually appealing compositions.
While GPT-4o 34 produces visually coherent and semantically meaningful results, it shows two consistent weaknesses. First, background preservation: GPT-4o often modifies the surrounding background, introducing shifts in color or texture that reduce spatial consistency and thus worsen FID and LPIPS. Second, object fidelity: for certain objects, the generated results exhibit deviations from the reference geometry or texture, which contributes to lower SSIM and PSNR scores. The visualized results of the comparison shown in Figure

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Masked Background
Reference Image
MimicBrush
GPT-4
Ours
Figure 9: Our RoomEditor compares its visual results with those of Mimicbrush on RoomBench.
and GPT-4o 34
Method
FIDJ
SSIM
PSNR1
LPIPS.
CLIP
DINO!
MimicBrush 7
24.99
0.761
18.42
0.135
87.06
75.13
RoomEditor (Ours)
21.16
0.766
19.83
0.120
88.90
81.46
Table 11:
Evaluation of robustness mask quality using bounding boxes masks. Our method maintains high performance even without accurate shape priors.
C.4 Robustness to Mask Quality
To further assess the robustness of model to imperfect masks, evaluated it using coarse bounding boxes as input masks, thereby removing any explicit shape prior and simulating practical scenarios where precise segmentation is unavailable.
As described in(Appendix our training strategy already introduces imperfect masks through random perturbations and simplified bounding boxes, encouraging the model to learn shape-consistent reconstruction even under imprecise guidance. During evaluation, we adopt a similar setup by replacing object masks with axis-aligned bounding boxes covering target regions.
As summarized Table 11, despite lacking detailed shape cues, our method outperforms Mim- icBrush 7 on most metrics, demonstrating strong robustness to mask variations and high-quality synthesis even with coarse or noisy masks-higee highligngg practicality for real-world editing scenar- ios.

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Light Intensity
0.6
0.8
1.0 (base)
1.2
1.4
MimicBrush 7
70.58
71.16 73.96
71.92
70.77
69.97
RoomEditor (Ours)
73.80
74.77
73.48
72.76
Table 12: Performance under different lighting intensities. Higher values indicate better performance.
Model
Params (M)
GPU Mem (GB)
Speed (samples/min)
PBE [33
1310
3.9
14.5
Door 6
2451
7.3
14
MimicBrush
2432
6.9
10.7
RoomEditor (Ours)
943
3.2
15.8
Table 13: Comparison of computational efficiency across different methods.
C.5 Robustness to Lighting Changes
To further evaluate the robustness of our method under varying illumination conditions, we conducted additional experiments with systematically adjusted lighting intensities. Since the original test does not explicitly account for lighting variation, we simulated different illumination conditions by scaling the brightness of background images. Specifically, brightness scaling factors of 0.6 and 0.8 (darker conditions), as well as 1.2 and 1.4 (brighter conditions), were applied to the entire test set.
We compared our method with MimicBrush 7 under these modified lighting conditions, and report the averaged results across all metrics (rescaled to a 0-100 range, with FID and LPIPS inverted for consistency). As shown in Table 12, although both methods slightly degrade under extreme lighting, our approach consistently outperforms (7] across all illumination levels, demonstrating strong robustness and generalizability to lighting variations without explicit modeling.
C.6 Computational Efficiency Analysis
To provide a fair comparison of computational efficiency across different methods, we report the model size, memory usage, and inference speed unified experimental setup. All experiments were conducted on single NVIDIA A6000 GPU in FP16 precision, using single-image inference with 50 denoising steps.
Table 13 summarizes model efficiency terms parameters (Params), GPU memory usage (Mem), and inference throughput (Speed). Our method strikes favorable balance between performance and efficiency-use usiiencyy-iinggg significantly fewer parameters and less memory than 77] while achieving about 1.5x higher inference speed demonstrating its suitability for practical deployment.
D Additional Visualization Results
In this section, we present additional qualitative results. Figure 10 showcases our results on the RoomBench dataset, while Figure 11 iillustrates our performance on the DreamBooth dataset. As observed, our method consistently achieves high fidelity and seamless harmony, demonstrating its effectiveness across both indoor environments and general scenarios. Finally, as shown Figure 12] qualitative results demonstrate that RoomEditor effectively integrates diverse objects into complex scenes, further validating the utility of RoomBench for real-world applications.
E Limitations and Future Work
Our method has two primary limitations, which plan address in future work.
First, the presence uncommon furniture items (such as clothes racks and microwaves) results in a long-tail distribution in our collected dataset. Consequently, our model may struggle to capture complex or rare furniture shapes, as the large intra-category variation geometry makes difficult for the dataset to fully represent all possible shape patterns. This occasionally leads to distorted

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Figure 10: Additional visualization results on the RoomBench dataset, demonstrating the high fidelity and seamless integration of furniture into indoor environments.
MA
BARA BIZZ
Additional visualization results the DreamBoof dataset, showcasing the performance of our method in various general scenarios with high fidelity and consistent harmony.
Figure 11:
inconsistent generations, illustrated in Figure 13 training samples for such items and the challenge of learning robust shape priors under limited data diversity.
The issue stems from both the insufficient
Second, our current approach does explicitly model complex visual phenomena such as object shadows and varying illumination conditions. Since the model edits only the masked region, shad- ows or lighting effects outside mask cannot synthesized. This limitation is inherent to masked-editing paradigm and is also shared by prior works such as PBE 53). AnyDoor (6), and MimicBrush [7]. These factors may significantly affect visual realism and remain challenging for both our method and existing approaches.
7

---

Booy Tppbl
Reference Image
Masked Background
PBE
Anydoor
Mimic[Brush
Ours
Figure 12: Visual comparison DreamBooth dataset 41
CIOREE
Source image
Source background
Reference image
Generated image
Figure 13: Failure cases illustrating distortions and inconsistencies when handling less common furniture items.
Additionally, given the structural differences between our model and recent DiT-based (6) diffusion architectures, adapting our formulation and analytical framework to more advanced DIT diffusion models represents a promising direction for future research.
We leave these aspects for future exploration.


Reviews:
[{"review_id": "SwWf3bDnEV", "reviewer": "Reviewer_BBjR", "summary": "The paper addresses the task of virtual furniture synthesis by contributing two main elements:\n\nRoomBench Dataset: A new large-scale benchmark containing 7,298 training pairs and 895 test pairs across 27 furniture categories, designed to evaluate both geometric consistency and visual realism.\n\nRoomEditor Method: A parameter-sharing dual U-Net architecture that enforces a unified feature space between the reference and background branches, leading to improved geometric alignment and harmonious composite results. Extensive experiments show that RoomEditor outperforms state-of-the-art baselines (PBE, AnyDoor, MimicBrush) on FID, SSIM, PSNR, LPIPS, CLIP-score, and DINO-score, and exhibits superior zero-shot generalization on the DreamBooth dataset.", "questions": "Please refer to the weaknesses.", "limitations": "Please refer to the weaknesses.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\n\nValuable Public Benchmark: RoomBench fills a notable gap in furniture synthesis by providing a high-quality, publicly available dataset that supports reproducible evaluation.\n\nElegant Architecture: The parameter-sharing dual U-Net is a simple yet effective design that directly addresses feature inconsistency in prior dual-branch networks.\n\nWeaknesses\n1.\tThe number of user studies is too small. A user study with only 10 people is not credible enough.\n\n2.\tThe failure case does not provide a detailed and comprehensive display and failure cause analysis, which made me suspect the results.\n\n3.\tThe method does not involve any explicit modeling of conditions such as lighting changes. I have doubts about the generalizability of the results of image composition under such conditions. Can you provide some examples with different lighting intensities to prove the generalizability of the method in other situations?", "was_revised": true, "final_justification": "Thank you for addressing my questions on Q3. As for Q1, I believe the results from various experiments effectively demonstrate the advantages of the proposed method.\n\nTherefore, I raised my rating.", "rebuttal": "## Sample Size of User Study\nWe thank the reviewer for pointing out this issue. Following the protocol in the MimicBrush paper [1], we initially conducted a user study with 10 participants. To strengthen the reliability of our results, we conducted an additional study with 15 more participants (total 25). The participants comprised 14 undergraduate students, 7 of their parents (to provide a non-technical perspective), and 4 university faculty or staff members, all of whom were explicitly informed of the evaluation criteria.\n\n| Method            | Fidelity Best (%) | Fidelity Rank↓ | Harmony Best (%) | Harmony Rank↓ | Quality Best (%) | Quality Rank↓ |\n| ------------------- | ------------------- | ----------------- | ------------------ | ---------------- | ------------------ | ---------------- |\n| PBE $^\\star$            | 5.1               | 3.36            | 18.0             | 2.85           | 4.5              | 3.34           |\n| AnyDoor$^\\star$        | 8.9               | 2.96            |  7.6              | 3.16           | 3.5              | 3.20            |\n| MimicBrush$^\\star$     | 29.4              | 2.11            | 30.5             | 2.16           | 38.3             | 1.80           |\n| RoomEditor (Ours) | **56.6**              | **1.57**            | **43.9**            | **1.82**           | **53.6**             | **1.66**           |\n\nThe findings remain consistent—our method outperforms existing approaches across all evaluated metrics. These updated results will be included in the revised version.\n\n\n## Failure Case and Cause Analysis\n\nThank you for the feedback. An initial failure case analysis was provided in the appendix. Here, we offer a more detailed explanation, as figures cannot be included.\n1. Our model may occasionally struggle with capturing complex furniture shapes, as the large intra-category variation in geometry makes it difficult for the dataset to fully represent all possible shape patterns, thereby affecting the learned shape priors (e.g., Figure 11 shows distorted shapes).\n2. Since our method edits only the masked region, shadows outside the mask cannot be generated. This is a common limitation shared by PBE [2], AnyDoor [3], and MimicBrush [1].\n\nIn summary, the main causes are **the diversity of real-world furniture types** and **inherent constraints of the masked-editing paradigm**. We will include more visualizations and in-depth analysis in the revised version.\n\n## Robustness to Lighting Changes\nThank you for raising this important point. Since our original test set does not account for lighting variation, we conducted additional experiments to evaluate the robustness of our model under different illumination conditions by adjusting the image brightness.\nSpecifically, we applied lighting intensity adjustments to the background images with scaling factors of 0.6 and 0.8 (darker), as well as 1.2 and 1.4 (brighter), and conducted evaluations on the full test set. We compared our method with MimicBrush and computed the average scores (all metrics were rescaled to a 0–100 range, with FID and LPIPS inverted), as shown below.\n\n|     Light intensity        |     0.6              |     0.8              |     1.0      |     1.2              |     1.4              |\n|-------------|----------------------|----------------------|--------------|----------------------|----------------------|\n| MimicBrush  |     70.58   |     71.16    |     71.92 (base)    |     70.77    |     69.97     |\n|     Ours    |     73.80    |     73.96     |     74.77 (base)    |     73.48     |     72.76     |\n\nSince our model design and data construction did not explicitly consider extremely dark or bright lighting conditions, there was a slight performance drop. However, it is worth noting that the degree of performance drop is comparable to that of MimicBrush, so our method still maintains a significant lead under the same settings. We will include lighting-related experiments, analysis, and visual results in the revised version.\n\n## References\n[1] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Heng shuang Zhao. Zero-shot image editing with reference imitation. Advances in Neural Information Processing Systems, 37:84010–84032, 2025.\n\n[2] Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18381–18391, 2023.\n\n[3] Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6593–6602, 2024."}, {"review_id": "3atFHKS06P", "reviewer": "Reviewer_myUv", "summary": "This paper proposes a new ready-to-use benchmark, RoomBench, for virtual furniture synthesis to overcome the lack of existing public benchmarks for this task. The paper further proposes a variation on the dual U-Net architecture, RoomEditor, by using parameter sharing between the two branches. The paper discusses the benefits and motivation for applying parameter sharing by mathematically modelling the editing task and relating it to the model. The paper also provides both qualitative and quantitative examples of superior performance of RoomEditor on RoomBench and DreamBooth datasets in terms of FID, SSIM, PSNR, and LPIPS. A user study is also conducted. Finally, an ablation study is conducted assessing integration of common techniques such as CLIP and other U-Net configurations.", "questions": "1. Could the authors provide further mathematical justification for equations (5), (6), (7)?\n2. Could the authors give more justification for the choice of baselines?", "limitations": "Yes - limitations are mentioned in the appendix", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths**\n\n1. **Quality:** The paper is complete. The paper establishes a diverse set of metrics. The paper demonstrates generalizability by evaluating RoomEditor on the DreamBooth dataset demonstrating a rigorous approach. A user study is also conducted that demonstrates clear preference for RoomEditor amongst real users.\n2. **Clarity:** The paper clearly explains the model architecture, while also providing intuition to support their choices by connecting it to approximations of their problem formulation compared to other dual U-Net architectures. Sufficient details are also given to enable reproducibility. \n3. **Significance:** The paper proposes an important new benchmark, RoomBench, in a field that has many real-world applications and lacks public datasets. The dataset is diverse in the types of furniture covered. The ablation study conducted shows potential for integration with other existing models, and also future approaches. The model is also general as it shows strong performance on DreamBooth despite only training on RoomBench.\n4. **Originality:** The dataset presented is new. The parameter sharing technique appears to be new and justified.\n\n**Weaknesses**\n\n1. **Quality:** Despite mentioning text-to-image editing models, the paper does not compare these existing models. Moreover, Diffuse-to-choose is mentioned as also focusing on furniture but not compared deeply. The mathematical and technical justification appears to rely on intuitive or heuristic arguments, without sufficient empirical evidence or theoretical guarantees.\n2. **Clarity:** There was not sufficient introduction to the models being compared to, i.e., PBE, MimicBrush, and AnyDoor and the justification of choosing these over other models is unclear.\n3. **Significance:** Since only a few models are compared to, the impact of the research is slightly diminished, as there are other strong models that could be considered.\n4. **Originality:** U-Nets are common in existing works surrounding image synthesis, and the parameter sharing approach is only a minor change to this existing work.", "was_revised": true, "final_justification": "Taking into consideration, the other reviews and rebuttals, I think the paper still deserves to be accepted.\n\nFor me, the authors have answered my questions in detail which has clarified all my raised issues. The contributions are strong, in particular, a new dataset and a new model.\n\nFurthermore, the authors have given reasonable rebuttals to most of the questions of the other reviewers. I assign a high weight to this because it demonstrates that in even more general conditions, their model is able to perform well.", "rebuttal": "## Baseline Choice\nThank you for the question. The following are relevant explanations.\n1. **Regarding Text-to-Image Models and Diffuse-to-Choose**: We sincerely appreciate the suggestion. However, text-to-image models are not directly applicable to our task, which involves generating a new object conditioned on both a masked background and a reference image—input modalities that such models are not inherently designed to handle. As for Diffuse-to-Choose [1], we acknowledge its relevance, but unfortunately, its code, model, and evaluation data are not publicly available at this time, which makes a fair and reproducible comparison currently infeasible.\n2. **Current Baseline Choice** : We selected three methods that were the most relevant, representative, and state-of-the-art at the time of our study, each covering a distinct architectural paradigm in example-based image composition.\n    * PBE [2] (line 97): A Stable Diffusion inpainting model that injects reference via CLIP visual encoder; suffers from information loss due to CLIP.\n    * AnyDoor [3] (lines 99-100): Uses ControlNet for background consitency and a stronger DINO encoder for reference integration.\n    * MimicBrush [4] (lines 101-102): Employs a dual U-Net architecture with explicit reference feature encoding.\n\n3. **Further evaluation with GPT-4o**:  Given the lack of directly comparable methods in existing work, we further evaluated GPT-4o on our dataset to benchmark against a more advanced model. We provided the reference image and the masked background as input, using the prompt: \"Place the object from the first image into the masked area of the second image.\" Due to API costs and long generation time, we randomly sampled 200 examples and compared GPT-4o with our method and MimicBrush.\n\n|     Methods       | CLIP-score (↑) | DINO-score (↑) |\n| ------------ | ------------ | ------------ |\n| Mimicbrush | $\\underline{88.12}$           | 79.49      |\n| GPT-4o     | 87.80      | $\\underline{83.30}$          |\n| Ours       | **90.42**      | **85.12**      |\n\nOur findings indicate that GPT-4o generates highly coherent images with realistic lighting. However, it sometimes struggled with object fidelity, where the generated object's shape or design deviated from the reference image. This is reflected in its lower CLIP-score and DINO-score compared to our method. We will include this new comparison in our revised paper. We believe benchmarking against a state-of-the-art model like GPT-4o provides a more comprehensive evaluation.\n\n## Mathematical Justification (Equations 5–7)\nWe are happy to clarify the reasoning behind our mathematical formulation. We acknowledge that our derivations are based on certain modeling assumptions. Below, we explain the rationale behind each equation in more detail.\n* **Equation (5)**: This equation is grounded in a key empirical property of inpainting models: they are trained to preserve unmasked regions with high fidelity. This arises from both the task design—where known regions are provided as input—and the loss function, which is applied over the entire image. We consider two different model inputs: (1) by masking the object in $\\boldsymbol{I}\\_{\\text{gt}}$ to get $\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}}$ and applying the aforementioned property, we find the model's prediction for the background region is essentially identical to that in $\\boldsymbol{I}\\_{\\text{gt}}$; (2) by masking the background in $\\boldsymbol{I}\\_{\\text{gt}}$ to retain only the object (represented as $\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$ in our paper) and applying the same property, we find the prediction for the object region is also essentially identical to that in $\\boldsymbol{I}\\_{\\text{gt}}$. Combining the masked outputs of these two ideal predictions would yield the ground truth $\\boldsymbol{I}\\_{\\text{gt}}$.\n* **Equation (6)**: Equation (6) generalizes the principle of Equation (5) from the pixel level to the feature level. This generalization is based on a key assumption regarding the U-Net's architecture, for which the rationale is two-fold: (a) convolutional and MLP layers operate locally, and (b) cross-attention mechanisms are primarily designed to inject object features into the masked region, thus having minimal impact on the unmasked background features. Based on this assumption, we posit that the features at locations corresponding to $\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}}$  (i.e., $f\\_{l}(\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}})$ ) and those corresponding to $\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$ (i.e., $f\\_{l}(\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$) can be combined to approximate the features of the complete ground-truth image $\\boldsymbol{I}\\_{\\text{gt}}$ (i.e., $f\\_{l}(\\boldsymbol{I}\\_{\\text{gt}})$ ).\n* **Equation (7)**: Equation (7) provides a high-level conceptual model of the reference encoding process. It assumes the existence of an ideal transformation, $\\mathcal{R}\\_{l}$, that maps the features of the input reference image to the features that would have been generated if the ground-truth object were present. In practice, different models learn different approximations of this mapping (see the discussion in Section 4.2.2 Comparison with Previous Works, line 194-213). Our formulation with parameter sharing is simply a novel and efficient way to learn this transformation.\n\nWe will update the explanation accordingly in the revised version.\n\n## Originality in the model architecture\nWe understand the reviewer’s concern about architectural novelty. However, we would like to highlight that our contributions extend beyond architecture and include data and in-depth analysis. We introduce a new modeling perspective by identifying a feature inconsistency issue commonly present during training in existing methods (validated in Line 214 and Figure 3), and address it through a parameter-sharing strategy. While this architectural modification is concise, it yields significant improvements in both performance and efficiency. In addition, to the best of our knowledge, we are the first to provide a mathematical analysis and corresponding empirical validation for this approach, which we believe offers the community concrete insights into this problem space.\n\n## References\n[1] Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar, Amir Tavanaei, and Ismail B Tutar. Diffuse to choose: Enriching image conditioned inpainting in latent diffusion models for virtual try-all. arXiv preprint arXiv:2401.13795, 2024.\n\n[2] Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18381–18391, 2023.\n\n[3] Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6593–6602, 2024.\n\n[4] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Heng shuang Zhao. Zero-shot image editing with reference imitation. Advances in Neural Information Processing Systems, 37:84010–84032, 2025."}, {"review_id": "wlP8oIv1Qy", "reviewer": "Reviewer_ThnS", "summary": "This paper proposes the RoomEditor method, which employs a parameter-shared dual U-Net architecture to enforce feature consistency learning. This addresses the feature misalignment issue caused by independent processing in traditional dual-branch architectures, enabling high-fidelity virtual furniture synthesis. Additionally,  They construct the RoomBench benchmark dataset and conduct experiments to validate the effectiveness and generalization capability of RoomEditor method.", "questions": "Does RoomEditor support multi-view images as input?\n\nGiven that the generated objects have high fidelity, does it also imply higher requirements for the reference images? For instance, if the reference images contain watermarks or mosaics, how does it affect the generation results?", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n● The core motivation of the paper is clearly written and easy to understand\n\n● High-fidelity virtual furniture synthesis is a useful task for many applications\n\n● The authors validate the generalization ability of RoomEditor in scenes beyond indoor environments\n\nWeaknesses:\n\n● RoomEditor still relies on high-quality predefined mask, and the effect of using low-quality masks has not been investigated in experiments, which somewhat restricts its practical applicability.\n\n● The related methods presented in the paper are insufficient, as several advanced image editing approaches (e.g., IN-CONTEXT LORA) and relevant furniture synthesis models (e.g., HomeDiffusion) are neither introduced nor compared.\n\n● The test set may implicitly overlap in distribution with the training set, and cross-dataset evaluation (e.g., on 3D-FRONT) has not been conducted to validate its generalization ability.\n\n● When comparing to other methods, detailed data on the computational resources and time consumption for each approach are missing.", "was_revised": true, "final_justification": "Although the GPT-4o comparison in the rebuttal raises some concerns about the metrics, the additional experiments and materials provided strengthen the paper. I therefore raise my final score to 'Borderline accept'.", "rebuttal": "## Robustness to Mask Quality\nWe thank the reviewer for raising this important point. We fully agree that a model's performance with low-quality masks is a critical measure of its practical utility, and we have in fact discussed this specific issue in our work. Our methodology reflects this discussion in two key stages:\n1. **Robustness in Training**: To better simulate real-world user scenarios, our training process (detailed in Appendix B, line 482-484) intentionally uses imperfect inputs. This includes using random perturbations and simple bounding boxes to make the model robust against imprecise segmentation (see Figure 6).\"\n2. **Realistic Evaluation**: Similarly, to simulate real-world scenarios, we instructed annotators to create intentionally coarse masks for the test set (as stated on lines 484-486). Consequently, all results reported in our paper are based on these coarse-grained annotations, not high-quality segmentations, with examples illustrated in Figure 7(b).\n\nTo further evaluate the robustness of our model, we add an experiment to our RoomBench using bounding boxes as masks, without any shape prior. The results are sufficiently illustrative, as shown below.\n\n| Models     | FID (↓) | SSIM (↑) | PSNR (↑) | LPIPS (↓) | CLIP (↑) | DINO (↑) |\n| ------------ | ---------- | ----------- | ----------- | ------------ | ----------- | ----------- |\n| MimicBrush | 24.99    | 0.761     | 18.42     | 0.135      | 87.06     | 75.13     |\n| Ours       | **21.16**    | **0.766**     |  **19.83**     | **0.120**      | **88.90**     | **81.46**     |\n\nDespite the lack of shape guidance, our model still outperforms MimicBrush, which has been trained on large-scale data and is known for its robustness. This provides strong evidence that our method is highly robust to mask variations.\n\n## Insufficient Related Methods \nWe sincerely thank the reviewer for pointing out these advanced approaches. In our revised paper, we will add a detailed discussion of both In-Context LoRA [1] and HomeDiffusion [2] to the related work section, summarizing their core contributions as follows:\n* **In-Context LoRA** leverages in-context learning to generate thematically consistent images from a few visual examples at inference time.\n* **HomeDiffusion** employs a two-stage strategy, first learning generation from multi-view references and then focusing on object-background integration.\n\nHowever, a direct quantitative comparison with these methods is unfortunately infeasible. In-Context LoRA's task formulation differs significantly from ours, as it does not take two images (background and reference) as input or support precise object placement. While HomeDiffusion is more related to our task, its code and benchmark are not publicly available, precluding a fair comparison.\n\nTo further strengthen our evaluation and address the reviewer's valid concern about comparing with SOTA models, we selected the commercial, closed-source model GPT-4o, which supports multi-image inputs and demonstrates impressive image editing capabilities. Due to cost and time constraints, we randomly sampled 200 examples from the RoomBench dataset and compared GPT-4o with our method and MimicBrush [3] on this subset.\n\n|            | CLIP-score | DINO-score |\n| ------------ | ------------ | ------------ |\n| Mimicbrush | $\\underline{88.12}$            | 79.49      |\n| GPT-4o     | 87.80      | $\\underline{83.30} $        |\n| Ours       |  **90.42**      | **85.12**      |\n\nOur qualitative analysis reveals that while GPT-4o often produces coherent images with realistic lighting, it struggles with preserving object fidelity, frequently altering the shape and design of the reference object. In contrast, our method achieves superior fidelity, which is reflected in its higher CLIP-score and DINO-score. We will include this comparison in the revised version.\n\n## On Generalization and Cross-Dataset Evaluation\nThank you for raising this important point. We address these concerns with existing clarifications and new experiments as follows:\n* **Strict Data Separation**: To prevent data leakage between training and testing sets, we ensured a strict separation of data sources during the collection of our RoomBench dataset. We will explicitly state this protocol in the revised paper to ensure clarity on the validity of our results.\n* **Existing Generalization Evidence**: We would like to gently point out that our original paper already demonstrated strong generalization to non-furniture objects through experiments on the DreamBooth dataset [4] (Table 3 and section 5.3, line 277). This experiment shows that our model, although not trained on generic data, outperforms models trained on large-scale generic datasets. This demonstrates the strong generalization capability of our approach.\n\n* **New Cross-Dataset Evaluation on 3D-FUTURE**: In addition, in response to the reviewer’s suggestion of testing on alternative furniture composition datasets, we evaluated our model on the 3D-FUTURE [5] dataset. We selected 1,020 samples covering 34 furniture categories (randomly 30 images per category). Since the dataset does not provide explicit image pairs, we applied horizontal flipping to reference objects and blurred the masks to avoid trivial copy-paste solutions. The results are as follows:\n\n| Metric     | FID (↓) | SSIM (↑) | PSNR (↑) | LPIPS (↓) | CLIP (↑) | DINO (↑) |\n| ---------- | ------- | -------- | -------- | --------- | -------------- | -------------- |\n| Mimicbrush | 14.20 | **0.6595**   | 20.64  | 0.272    | 80.10         | **64.61**         |\n| Ours       | **13.91** | 0.6581   | **20.93**  | **0.260**    | **80.28**         |  **66.97**         |\n\nAlthough our model was not trained on 3D-FUTURE, it achieves consistently better performance than Mimicbrush across multiple metrics, highlighting its strong generalization ability. We will incorporate all these clarifications and new results into our revised paper.\n\n## Computational Resources and Time Consumption\nThank you for raising this question. We report the total number of parameters and trainable parameters for different architectures in Table 4: Ablation Study. To enable a more comprehensive evaluation—including speed and memory consumption—we provide a comparative summary in the following table. All experiments were conducted using a single A6000 GPU in FP16 mode on our test set, with single-image inference and 50 denoising steps.\n\n| Models     | Params (M) | GPU Mem (GB) | Speed (samples / min) |\n| ---------- | ---------- | -------- | --------------------- |\n| PBE         |   $\\underline{1310}$   |   $\\underline{3.9}$    |   14.5                        |\n|   AnyDoor |   2451            |  7.3     |         $\\underline{14}$      | \n| Mimicbrush | 2432       | 6.9       | 10.7                 |\n| Ours       | **943**        | **3.2**       | **15.8**                |\n\nAs shown, our method requires significantly fewer parameters and less GPU memory compared to Mimicbrush, while also achieving faster generation speed.\n\n## Multi-view images\nThank you for this valuable question. Our model currently operates on single-view images. While we certainly acknowledge the potential of multi-view inputs, this direction was not prioritized in the current scope due to our specific research focus and challenges in data availability. We will ensure this is highlighted as a promising avenue for future research in our revised conclusion.\n\n## Quality of reference images\nThis is a practical and important concern. Our experiments show that when the reference object includes watermarks or mosaic artifacts, the model tends to perceive them as integral parts of the object and thus fails to remove them during generation. Improving robustness to such artifacts is a valuable direction we plan to explore in future work.\n\n## References\n[1] Lianghua Huang, Wei Wang, Zhi-Fan Wu, Yupeng Shi, Huanzhang Dou, Chen Liang, Yutong Feng, Yu Liu, and Jingren Zhou. In-context lora for diffusion transformers. arXiv preprint arXiv:2410.23775, 2024.\n\n[2] Guoqiu Li, Jin Song, and Yiyun Fei. Homediffusion: Zero-shot object customization with multi-view representation learning for indoor scenes. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 4698–4706, 2025.\n\n[3] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Heng shuang Zhao. Zero-shot image editing with reference imitation. Advances in Neural Information Processing Systems, 37:84010–84032, 2025.\n\n[4] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 22500–22510, 2023.\n\n[5] Huan Fu, Rongfei Jia, Lin Gao, Mingming Gong, Binqiang Zhao, Steve Maybank, and Dacheng Tao. 3d-future: 3d furniture shape with texture. International Journal of Computer Vision, 129:3313–3337, 2021."}, {"review_id": "4pOI0UXx3D", "reviewer": "Reviewer_VeYd", "summary": "The authors introduce a new dataset and propose a model specifically tailored for the furniture synthesis task. By training their model using a shared feature extractor on this dataset, they are able to generate realistic composite images. Experimental results show that the proposed method outperforms existing approaches.", "questions": "Major:\n- Eq. 5 is confusing. The diffusion model $\\epsilon_{\\theta}$ does not take the ground truth image as input. Based on my understanding, it should be $\\epsilon_{\\theta} \\approx \\mathcal{R}$. This makes the derivation of Eq. 6 and 7 difficult to follow.\n- For the experiments, in the training of MimicBrush$^{\\dagger}$, is the reference U-Net kept fixed or is it updated? A comparison with a model where the reference U-Net is not fixed would be more informative, as a trainable reference U-Net could learn features that align better with the imitative U-Net. Qualitative results for this variant would also be valuable.\n- The motivation for incorporating CLIP in the ablation study is unclear. RoomEditor already includes an image encoder—why is an additional encoder (CLIP) necessary, and how is it integrated with the existing architecture?\n- The metric Fidelity Rank in Table 2 is not clearly defined in the paper.\n- For the user study, the background of the annotators is not clearly described, which could influence the subjective results. Additionally, the study includes only 10 annotators, which seems insufficient for drawing reliable conclusions.\n- The claim made in Line 80 is not entirely accurate. Virtual try-on methods must also model the interaction between garments and the human body to ensure coherent integration. Ignoring this interaction often leads to artifacts or inconsistencies in the generated images.\n\nMinor:\n- The term \"inconsistency\" in Line 119 is vague—please clarify what kind of inconsistency is being referred to.", "limitations": "yes", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "One of the strengths of this work lies in the collection of a paired dataset comprising background images and corresponding reference images of furniture, along with annotated masks. The authors propose using a shared feature extractor for both the reference and background images, which contributes to realistic image synthesis. Although the model is trained specifically on furniture data, it has the potential to generalize to other object categories.\n\nHowever, the overall contribution of this paper appears limited. The proposed model architecture builds on [7], and the primary improvement—introducing a shared feature extractor—while effective, is relatively incremental. Although the authors discuss the advantages of RoomEditor in Section 4.2 and compare it with existing methods, the theoretical analysis in Section 4.2.1 is somewhat unclear and does not convincingly highlight the benefits of the approach. Moreover, the differences between RoomEditor and [7] are not particularly substantial. In addition, there are concerns regarding the experimental evaluation (see below).", "was_revised": true, "final_justification": "Consider the limited contribution of this work, I keep my rating.", "rebuttal": "## Mathematical derivation\nThank you for raising this question. We agree that, in general, diffusion models do not take ground truth images as input. However, for inpainting tasks, it is common practice to provide the masked image (in our case, the masked background $\\boldsymbol{I}_{\\text{bg}}^{\\text{M}}$) as input, so that the model can preserve the content in the unmasked regions. Since the model generates content over the entire image, this implies that a portion of the ground truth (i.e., the unmasked area) is effectively included in the input. Therefore, a characteristic of inpainting diffusion models is that **their predictions in the unmasked regions can be considered as ground truth.**\n\n**Equation (5)**: Based on this understanding, we consider two different model inputs. (1) By masking the object in $\\boldsymbol{I}\\_{\\text{gt}}$ to get $\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}}$ and applying the aforementioned property, we find the model's prediction for the background region is essentially identical to that in $\\boldsymbol{I}\\_{\\text{gt}}$; (2) by masking the background in $\\boldsymbol{I}\\_{\\text{gt}}$ to retain only the object (represented as $\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$ in our paper) and applying the same property, we find the prediction for the object region is also essentially identical to that in $\\boldsymbol{I}\\_{\\text{gt}}$. Combining the masked outputs of these two ideal predictions would yield the ground truth $\\boldsymbol{I}\\_{\\text{gt}}$.\n\n**Equation (6)**: It generalizes the principle of Equation (5) from the pixel level to the feature level. This generalization is based on a key assumption regarding the U-Net's architecture, for which the rationale is two-fold: (a) convolutional and MLP layers operate locally, and (b) cross-attention mechanisms are primarily designed to inject object features into the masked region, thus having minimal impact on the unmasked background features. Based on this assumption, we posit that the features at locations corresponding to $\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}}$  (i.e., $f\\_{l}(\\boldsymbol{I}\\_{\\text{bg}}^{\\text{M}})$ ) and those corresponding to $\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$ (i.e., $f\\_{l}(\\mathcal{R}(\\boldsymbol{I}\\_{\\text{ref}}^{M})$) can be combined to approximate the features of the complete ground-truth image $\\boldsymbol{I}\\_{\\text{gt}}$ (i.e., $f\\_{l}(\\boldsymbol{I}\\_{\\text{gt}})$ ).\n\n**Equation (7)**: It provides a high-level conceptual model of the reference encoding process. It assumes the existence of an ideal transformation, $\\mathcal{R}\\_{l}$, that maps the features of the input reference image to the features that would have been generated if the ground-truth object were present. In practice, different models learn different approximations of this mapping (see the discussion in Section 4.2.2 Comparison with Previous Works, line 194-213). Our formulation with parameter sharing is simply a novel and efficient way to learn this transformation.\n\n## Trainable Reference U-Net\n* **Is the reference U-Net kept fixed or is it updated**: The reference U-Net is kept frozen (to remain consistent with MimicBrush).\n* **Trainable reference U-Net**: This is an insightful point, and we have actually explored it in our ablation study. As shown in Table 4, “Dual U-Net (Frozen g) + CLIP” corresponds to MimicBrush$^{\\dagger}$, while “Dual U-Net (Trainable g) + CLIP” represents the variant suggested by the reviewer. The results show that training the encoder $g(\\cdot)$ leads to some performance improvement. However, it still falls short of the performance achieved by using a shared encoder $f(\\cdot)$ for both inputs. This underscores the effectiveness of our design choice to adopt a single encoder in terms of both simplicity and performance.\n* **Qualitative results of trainable reference U-Net**: We have not included the relevant qualitative results in the current version of the paper, but we will add them along with corresponding analysis in the revision. In our observations, although the overall harmony between the background and object is relatively high, using two separate encoders often leads to noticeable inconsistencies between the generated object and the reference in terms of color, shape, and pattern.\n\n\n## The Motivation for Incorporating CLIP in the Ablation Study\nThank you for this question. As noted in lines 157–159, mainstream dual U-Net architectures (such as MimicBrush) typically incorporate an additional image encoder. In contrast, our position is that this additional encoder can be removed, as its functionality largely overlaps with the reference U-Net, making it redundant. We validate this claim through ablation studies demonstrating its dispensability (see Table 4).\n\n## The metric Fidelity Rank in Table 2\nThank you for pointing this out. Fidelity refers to the identity consistency between the reference object and the generated object in terms of color, shape, pattern, and other visual attributes. We will clarify this in Section 5.2.2 (User Study) of the revised version.\n\n## Number and Background of User Study\n* **Number of user study**: Following the protocol in the MimicBrush paper [3], we initially conducted a user study with 10 participants for consistency. To enhance the reliability of our results, we later expanded the study to include **25** participants in total.\n* **Background of user study**: The participants consisted of 14 undergraduate students, 7 parents of some of the students, and 4 university faculty or staff members, all of whom were informed of the specific evaluation criteria. \n* **Updated results**: The findings remain consistent—our method outperforms existing approaches across all evaluated metrics. These updated results will be included in the revised version.\n\n| Method            | Fidelity Best (%) | Fidelity Rank↓ | Harmony Best (%) | Harmony Rank↓ | Quality Best (%) | Quality Rank↓ |\n| ------------------- | ------------------- | ----------------- | ------------------ | ---------------- | ------------------ | ---------------- |\n| PBE $^\\star$ [1]           | 5.1               | 3.36            | 18.0             | 2.85           | 4.5              | 3.34           |\n| AnyDoor$^\\star$  [2]      | 8.9               | 2.96            |  7.6              | 3.16           | 3.5              | 3.20            |\n| MimicBrush$^\\star$ [3]    | 29.4              | 2.11            | 30.5             | 2.16           | 38.3             | 1.80           |\n| RoomEditor (Ours) | **56.6**              | **1.57**            | **43.9**            | **1.82**           | **53.6**             | **1.66**           |\n\n## Response to the Comment on Virtual Try-on Methods  \nWe appreciate the reviewer’s comment. Virtual try-on indeed involves modeling complex garment-body interactions. However, our task differs in that furniture synthesis requires handling diverse furniture shapes, whereas clothing in virtual try-on applications typically exhibits more consistent shapes. We will revise the relevant statement in the manuscript to better clarify these distinctions.\n\n\n## Response to Claim about inconsistency\nThank you for raising this point. Here, \"inconsistency\" refers to discrepancies between the object or color in the reference image and those in the background image—in other words, they do not depict the same object. For example, in Appendix Figure 5, the left image in the second row (background image) and the right image in the same row (reference image) clearly correspond to different objects. Such cases are quite common in practice, as a single product may have multiple versions or color variants.\n\n## References\n\n[1] Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 18381–18391, 2023.\n\n[2] Xi Chen, Lianghua Huang, Yu Liu, Yujun Shen, Deli Zhao, and Hengshuang Zhao. Anydoor: Zero-shot object-level image customization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6593–6602, 2024.\n\n[3] Xi Chen, Yutong Feng, Mengting Chen, Yiyang Wang, Shilong Zhang, Yu Liu, Yujun Shen, and Heng shuang Zhao. Zero-shot image editing with reference imitation. Advances in Neural Information Processing Systems, 37:84010–84032, 2025."}]

Author rebuttal:

Meta-review: This paper presents a timely and valuable contribution to the field of virtual furniture synthesis by introducing RoomBench, a new ready-to-use public benchmark, and RoomEditor, a dual U-Net model with a parameter-sharing mechanism.

The establishment of the RoomBench dataset addresses a significant gap in the field, providing a standardized and diverse platform for evaluating furniture layout synthesis tasks. This benchmark alone constitutes a meaningful contribution to the community, enabling fair comparisons and future research.

The proposed RoomEditor model, while building upon established U-Net architectures, introduces a well-motivated parameter-sharing approach between its dual branches. The authors provide not only intuitive explanations but also mathematical modelling to justify their design choices, adding theoretical depth to their methodological innovation.

The experimental validation is comprehensive and rigorous. The model demonstrates strong performance across multiple metrics (FID, SSIM, PSNR, LPIPS) on both RoomBench and the DreamBooth dataset, showing excellent generalization capability. The inclusion of a user study is particularly valuable, as it aligns with human-centric evaluation principles that are crucial for practical applications in interior design.

While the paper could be strengthened by more extensive comparisons with recent text-to-image editing models and specialized furniture synthesis methods (e.g., Diffuse-to-choose), and while the theoretical justification could be further empirical, these limitations do not diminish the overall significance of the contributions.

The combination of a novel benchmark, a well-designed model with thoughtful architectural modifications, comprehensive evaluation, and demonstrated generalizability makes this paper a solid contribution worthy of acceptance. The RoomBench dataset has particular potential to become a standard evaluation platform in this emerging research area.
Decision: Accept (poster)