Object Fidelity Diffusion for Remote Sensing Image Generation
Authors: Ziqi Ye
Shuran Ma
Jie Yang
Xiaoyi Yang
Yi Yang
Ziyang Gong
Xue Yang
Haipeng Wang
Venue: ICLR 2026 Poster (2026)
Keywords: Image generation
remote sensing
Primary area: generative models

Abstract: High-precision controllable remote sensing image generation is both meaningful and challenging. Existing diffusion models often produce low-fidelity objects due to their inability to adequately capture morphological details, which may affect the robustness and reliability of object detection models. To enhance the accuracy and fidelity of generated objects in remote sensing, this paper proposes Object Fidelity Diffusion (OF-Diff), which effectively improves the fidelity of generated objects. Specifically, we are the first to extract the prior shapes of objects based on the layout for diffusion models in remote sensing. Then, we introduce a self-distillation diffusion model with consistency distillation loss, which can generate high-fidelity remote sensing images without providing real images during the sampling phase. Furthermore, we introduce DDPO to fine-tune the diffusion process, making the generated remote sensing images more diverse and semantically consistent. Comprehensive experiments demonstrate that OF-Diff outperforms state-of-the-art methods in the remote sensing across key quality metrics. Notably, the performance of several polymorphic and small object classes shows significant improvement. For instance, the mAP increases by 8.3%, 7.7%, and 4.0% for airplanes, ships, and vehicles, respectively.

Published as a conference paper at ICLR 2026
OBJECT FIDELITY DIFFUSION FOR REMOTE SENSING IMAGE GENERATION
Ziqi Yel.2., Shuran Ma Jie Yang", X Yang', Yi Yang', Ziyang Gong',
Xue Yang* Haipeng Wang*!
Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University
Shanghai Innovation Institute Xiidian University
School of Automation and Intelligent Sensing SCS, Shanghai Jiao Tong University
Code http::///..ttoao.ooco//e/aaaaaa.
ABSTRACT
High-fidelity, controllable remote sensing layout-to-image generation is highly valuable for providing high-quality data for downstream object detection tasks. However, existing methods either rely on additional textual guidance, leading to geometric distortions, require extra real-image references, limiting practical applicability. To address these challenges, we propose Object Fidelity Diffusion (OF-Diff), which leverages object layouts extract structural shape priors and employs an online-distillation strategy to integrate complex image features. This allows the model to perform highly controllable, high-fidelity image generation at inference without relying on real-image references. Furthermore, we introduce DDPO to fine-tune tune the diffusion process, making the generated remote sensing images more diverse and semantically consistent. Comprehensive experiments demonstrate that OF-Diff outperforms state-of-the- methods in the remote sens- ing across key quality metrics. Notably, the performance of several polymorphic and small object classes shows significant improvement. For instance, the mAP increases by 8.3%, 7.7%, and 4.0% for airplanes, ships, and vehicles, respectively.
SOTA
TOETEN HAR L THE
T-SNE:
Leakege: spills outside specified layout
Structural Distortion: resulting malformed object morphology
Dense Generation Callapse:
close To real
crowded
OF-Diff
1-SNE:
No objects are generated in ren- specified layout areas,
shops of the object distorted.
position
number
objects
more accurate dense scenes.
Distributions closer images
(a)
(b)
(c)
Figure I: Critical failure modes in the SOTA method (CC-Diff): (a) Control Leakage; (b) Structural Distortion; (c) Dense Generation Collapse. Our OF-Diff (2nd row) effectively resolves these issues.
INTRODUCTION
Synthesizing high-fidelity, spatially-controllabee remote sensing (RS) images is critical frontier for overcoming the data limitations that hinder downstream perception tasks like object detection Yang et 2021 Zhang et al. 2020 Yang al. 2019). Current RS generation methods, however, typically rely either ambiguous text prompts (Kanna et al. 2023 Sebaq & ElHell 2024) or auxiliary conditions like semantic maps (Sebaq ElHelw 2024 Tang et al. [2024] Gong et al.
2024 Hu et al.) 2025) Jia et al.) 2025). While visually plausible, such guidance is fundamentally disconnected from the instance-level ground truth, failing to provide the precise control necessary for effective data augmentation.
Equal contribution. Corresponding author,
Project lead.

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Published as a conference paper at ICLR 2026
Input
Input
Input
34.3 gurs
VOLO Score (M)
Layout
Layout
Layout
17%
AeroGen
DC-DIT
Caption
Caption
FG&BG Exomples
Caption
FG Shapes
$3.4
Categories
Categories
Categories
LayouiDif
Stondard
Standard
Online-Distillatoon
524
LDM
LDM
LDM
Standard Sampling
Standard Sampling
DDPO
GGUGEN
Aeragen, LayoutDiff...
CC-Diff...
OF-Diff (Ours)
25
35 60
50
(a)
(b)
(c)
(d)
Figure 2: Comparison of OF-Diff with mainstream L2I methods. FG/BG stands for fore- ground/background. (a) by quality/quantity of patches from ground truth. (c) OF-Diff enhances fidelity via shape extraction and DDPO, without patch reliance. (d) Results demonstrate superiority.
baseline. (b) Added instance-based module, limited
In contrast, corouutto-Immaee (L21) generation conditioned on object bounding boxes offers a more robust solution for precise spatial control. This paradigm has been extensively studied in the natural image LayottDfuuoooiiiioonn (Zheng et al., 2023) treats it as a multi-modal fusion problem, 2023) enables open-workd world generation through additional control signals, and
GLIGEN (Li et al.
ODGen (huu et al. 2024) improves controllability by decoupling objects-yet its direct application
remote sensing (RS) imagery remains non-trivial due expansive backgrounds, arbitrary object
orientations, and densely packed scenes.
In RS layout-to-image generation, existing methods like AeroGen (Tang al. 2025) and CC-Diff (Zhang et al. 2024) take different approaches. AeroGen, a coarse layout-condttioned model, suffers from limited spatial and shape control. In contrast, instance-level methods like CC-Diff achieve higher controllability and fidelity by referencing real instances, but this creates heavy dependence on the quality and quantity of real data, limiting generalization and flexibility. The images generated via CC-Diff diverge more markedly from the real remote sensing data distribution, aligning instead with the style characteristic of the model's pre-training corpus. We summarize common failure modes (see Figure []. including control leakage, structural distortion, dense generation collapse and feature-level mismatch.
These deficiencies significantly degrade the performance on object detection tasks, limiting their practical application in intelligent RS interpretation. In this paper, we introduce Object Fidelity Diffusion Model (OF-Diff). It is designed to improve the shape fidelity and layout consistency of object generation in RS images. As shown in Figure 2 the existing L.21 methods are mainly di- vided into two categories. The first is layout-conditioned baseline, shown Figure a like Aerogen and LayoutDiffusion. The second is the method with instance-based module, as shown in Figure b)) like CC-Diff. However, such methods require real instances and images as references during the sampling stage in order to generate high-quality synthetic images. In contrast, OF-Diff generates high-fidelity remote-sensing objects using only the foreground shape, and subsequently employs disti--iitiilaation to further align the outputs with real images, as shown in Figure c In addition, it fine-tunes the diffusion with DDPO, effectively enhancing the performance of down- stream tasks for the generated images. The results in Figure d) demonstrates the superiority of OF-Diff over other methods. Our contributions are summarized as follows:
We introduce OF-Diff, an online-distillation controllable diffusion model with prior shape extraction, which improves generation fidelity while reducing reliance on real images, en- hancing practical applicability.
We propose a controllable generation pipeline that fine-tunes diffusion models with DDPO for remote sensing images, further boosting fidelity and diversity.
Extensive experiments demonstrate that OF-Diff generates high- figh-fideiity, layout- and shape- consistent images with dense objects, and serves as an effective enhancement for object detection tasks.
2

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Published as a conference paper at ICLR 2026
2
RELATED WORK
2.1 ADVANCES IN IMAGE GENERATION
Diffusion models (Dhariwal Nichol 2021 Ho et 2020 Kingma al. 2021 have increas-
ingly replaced Generative Adversarial Networks (GANs) (Goodfellow et al. 2014 Karras et al.)
2021 and Variational Autoencoders (VAEs) (Kingma et al. 2013] Rezende al. 2014) in image synthesis tasks due to their training stability and superior output quality. Recent ad adncee in efficient samplers, such as DDIM (Song et al. 2021), Euler (Karras et al. 2022). and DPM-Solver (Lu al., 2022), have further improved the practicality. Latent Diffusion Models (LDMs) (Rombach et al., 2022b), which operate in low-dimensional latent spaces, significantly reduce computational costs while preserving visual fidelity. The success of models like DALL-E2 (Ramesh et al., (022) and Im- agen (Saharia et al., 2022) demonstrates how this paradigm supports training on vast internet-scale datasets. As a result, diffusion-based approaches now provide strong foundation for high-quality image generation.
2.2 LAYOUT-TO-IMAGE GENERATION
Controllable image synthesis primarily includes text-to-image (T2I) and layout-to-image (L2I) gen- eration. While T21 models (Nichol et al. 2022 Ramesh et al., 2022) achieve semantic alignment via textual prompts, L.2I methods offer better spatial control. Recent works enhance layout condition- ing through layout-as masdmltyyy designs (Zheng et al.] (023), gated attention (Li et al.. 2023), and instance-wiss wise generation (Wang et al., 2024 Zhou et al. 2024). However, these methods rely solely on coarse layout inputs (e.g., bounding boxes), which lack fine-grained shape information critical for synthesizing morphologically complex objects.
2.3 REMOTE SENSING IMAGE SYNTHESIS
Synthesizing high-fidelity training data is crucial for advancing remote sensing (RS) object detec- tion, a field critical to numerous applications, but often hampered by the scarcity of extensively an- notated datasets. Despite its necessity. most generative models for RS imagery, such as DiffusionSat (Khanna et al. (023) and RSDiff (Sebaq & ElHelw] 2024), still rely on coarse semantic guidance. While other approaches leverage diverse control signals (Tagg et al., 2024) like OpenStreetMaps (Espinos&& & Crowley| 2023), they are generally not optimized for the bounding box format central to object detection. This naturally motivates 1.21 approaches including AeroGen (Tang et al.) 2025) and CC-Diff (Zhang al.) (024)) which have improved spatial accuracy and contextual consistency through layout-mask attention and FG/BG dual re-samplers. However, they suffer from limited controllability and heavy reliance real data.
3 METHOD
3.1 PRELIMINARY
Diffusion models (Song et al. 2021 aim to capture the underlying data distribution p(z) by it- eratively reconstructing data from noisy representation that is initially sampled from standard normal distribution. Denoising Diffusion Probabilistic Models (Ho et al., 2020) parameterize the model the function eo c0 t) predict the noise component of the sample x; at any time step The training objective is to minimize the mean squared error (MSE) loss between the actual noise é and the predicted noise cx t):
C = C=BrAAAeNNOO [lle- (z,, ))]
Stable Diffusion (SD) (Robaach al., 2022b Qiu al., 2025) utilizes a pre-trained VQ- TQ-VAE Van Den Oord et al. (017) to encode images into lower-dimensional latent space, and trains the diftusion model in this latent domain on the representation E Z. At diffusion step 6, the training loss under the conditions of text prompt e and control conditions ce can be written as:
L = Eee. [ile- ea 4, ca, (,,, co)]
(2)
3

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Published as a conference paper at ICLR 2026
Training
Sompling
ES6M
Dragel
images are requirez es nainto excellent fidelity
SARU
Imoge
Lonel
label
VQ-VAE
ESGM
Zo
SD Encoder Blocks ?
Z
Blocks +
SD Encoder
Zero Gonvs
SD Encoder Blocks
Zero Conv
Duncising
Cortroliet
ControlNet
Locoliarioon
Erfonced Shape
Festure
Shope Feature Decoder 19
SD Decoder
SD becader
tranbble
* frezen
Diffusion precess
feature branch
Image feature branch
Mix feature branch
(0)
Consistency Distilation Leos
(b)
Z..1
Z'
self distillation branch
prep-grodient
Figure 3: OF-Diff's overall architecture. (a) During training, object shape features extracted by ESGM and image features are processed by ControlNet, and the resulting information used to update stable diffusion decoders via moline-distillation. (b) During sampling, only the label and the shape feature stable diffusion decoder are used to generate synthetic images. (c) Architecture of the Enhanced Shape Generation Module (ESGM).
where L represents the overall learning objective of the complete diffusion model. This objective function is explicitly applied during the fine-tuning of diffusion models conjunction with Con- trolNet (Zhang al. 2023).
3.2 ARCHITECTURE OF OF-DIFF
As illustrated in Figure 3 a), the training of OF-Diff requires both real images and their correspond- ing labels. First, for ControlNet, the real image and its label are processed by the Enhanced Shape Generation Module (ESGM) to extract the object mask. The image and mask are then fed into ControlNet obtain the image feature c and the shape feature c.. To enrich the structural-only shape prior with richer appearance and contextual cues from the image, we combine them into a mix-feature Cr. which will later serve teacher input in online-distillation. Concretely:
Cm
n N
G. + sg [ca].
where n denotes the current iteration number, and N is the total number of iterations. In order to enable the prediction conditioned on mix-feature serve as a stable anchor point, improve the morphological fidelity of the generation, we adopt stop-gradient strategy (Chen & He 202T) for C₂ when calculating Cm-
Second, for Stable Diffusion, the input image is first compressed into latent space features by a pre-trained VQ-VAE. Then, concatenated with Gaussian noise e to form ž,. After passing through the SD encoder blocks, the feature Z, is fed into a dual-decoder architecture. One branch, the shape-fcature SD decoder, conditions on cai the other, the mix-feature SD decoder, conditions on Gy- Their reconstruction losses defined as Ly and Las, respectively:
c = = co Ca), C, = ||c - c|2
(4)
|| cl
c. co (z,, t, Ct. Cm),
(5)
Third, for online distillation, the mix-feature SD produces more accurate predictions thanks to its stronger image prior, but needs real images, limiting diversity. In contrast, the shape-feature SD
4

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Published as a conference paper at ICLR 2026
supports arbitrary label control but risks converging to low-fidelity local minima. To reconcile these trade-offs, we propose an
framework with a consistency loss Le:
C.
110 - sg [=
(6)
Here, the prediction & from mix-feature SD decoder acts as stop-gr nop-gradient teacher signal, serving as anchor to guide the prediction eg from shape-feature SD decoder towards high-fidelity optima parameter space.
The overall training objective is therefore:
C- Lym XLes
During the sampling phase, illustrated in Figure 3b), only the frozen ControlNet and the shape feature stable diffusion are utilized with arbitrary label prior control to synthesize RS images.
3.3 ENHANCED SHAPE GENERATION MODULE
In natural imagery, perspective and scale changes prevent a unique geometric model for most ob- jects. Conversely, remote-sensing objects display quasi-invariant shapes. For instance, courts rectangular, chimneys and oil tanks circular, and airplanes bilaterally symmetric with a distinct nose and tail. This shape consistency enables the use of masks impose strong controllability on im- age synthesis for remote sensing. To better leverage category labels for object shape extraction, we introduce the Enhanced Shape Generation Module (ESGM, see Figure Bie)). During the training phase, ESGM uses paired images and labels to generate precise object masks. And at sampling time, employs learned shape priors to synthesize diverse masks object shape.
For the given image x; and its bounding box v corresponding to category j (j E [1, we first utilize the RemoteCLIP (Liu et al. 2024) generate textual description of the object enclosed within the bounding box. With this description and the original image 2₁ the RemoteSAM (Yao) et al. 2025) then generates the corresponding shape masks {-]}-
In the shape augmentation phase, each object mask xa is cropped by its bounding box y randomly rotated, and placed back onto blank canvas to produce shape-enhanced mask. During training, ESGM uses real image shapes; at sampling, it selects enhanced shapes from lightweight mask pool collected during or after training. our experiments, we use masks generated during training.
3.4 DDPO FINE-TUNING
To enhance the diversity of the distribution of data generated by the fine-tuned model and maintain better consistency with the distribution of real images [Schulman et al. (2015) 2017), denoising dif- fusion policy optimization (DDPO) Black al. (2023) is applied the post-training of DFffiff. DDPO regards the denoising process of the diffusion model multi-step Markov decision pro- cess (MDP) (for a detailed derivation, please refer to the Appendix A.2). To optimize the policy
(a; st) so to maximize the cumulative reward Br-p(-mm! En R(s; ai) the gradient is computed as follows:
(x t, Xt) Par (x--1 1
-Txo,, c) Vo Vologpo(x--1 ( t, xt)
(8)
C, 1, xx)
r(xo, c) (KNN xo) wXL. x6))
(9)
The reward functions based on K-Nearest Neighbor (KNN) and KL divergence introduced, re- spectively, to optimize the diversity of generated data and the distribution consistency between gen- erated data and real data. is the weight parameter, and x is the real image in the dataset. Following standard practice, we compute the KNN the low-dimensional embedding space CLIP's image encoder. The implementation details are shown in Appendix A.2

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4 EXPERIMENTS
4.1 EXPERIMENTAL SETTINGS
Datasets. DIOR-R (Cheng et al. 2022), the rotated variant of DIOR (Li et al. 2020). contains 20 categories annotated with oriented bounding boxes; we follow the offiiaa 1:1:2 split for train- iggvaliidaiiontestingg DOTA-v1.0 (Xia et al.) 2018) includes 15 categories featuring dense scenes and small objects. We crop the images from DOTA to 512 x 512 following MMRotate (Zhou al.) 2022), discarding those without valid objects. HRSC2016 (Liu et al. 2017) is high-resolution ship detection dataset with a multi-level hierarchical taxonomy. We use the finest- grest-grained level, consist- ing of 26 detailed ship categories. The experiments of this dataset are reported in Appendix A.4 Unless otherwise specified, we train the diffusion model on the trainset. For downstream detection, we use the trainset annotations as layout and mix generated samples with the real trainset, and report evaluation results on the testset.
Implementation Details. We train OF-Diff separately each dataset (DIOR/DOTA), based on Stable Diffusion 1.5 (Rombach al. 2022a) pretrained model. Only the ControlNet and shape feature SD decoder are fine-tuned, while all other modules remain frozen. The weighting coefficient of the consistency loss is set to the value in KNN is set to 50, and the weight w of the KL divergence is 2. Training is performed using the AdamW optimizer with learning rate of le-5. The global batch size is set to 64, and training runs for 100 epochs.
method with state-of-the-an art L2I generation models for both remote sensing (AeroGen (Tang et al. 2025). CC-Diff (Zhang et al. 2024)), and natural images (LayoutDiffusion (Zheng et al. 2023), GLIGEN al. 2023)). For fair comparison, all models are re-trained using our dataset settings, following their official training details respectively.
Benchmark Methods. We compare
Evaluation Metries. To more comprehensively evaluate the effectiveness of OF-Diff, adopt total of 13 metrics spanning 4 different evaluation aspects.
Generation Fidelity. We use FID (Heusel et al. 2017) and KID BBikkowkii et al. 2018)
assess perceptual quality, along with CMMD (Jayasumana al.) 2024). which measures CLIP feature distances between generated and real images to evaluate layout alignment.
Layout Consistency. We report CAS (Ravuri & Vinyals 2019) using pretrained clas- sifier assess object recognizability, and YOLOScore by applying pretrained Oriented R-CNN (Xie et al. 2022) (w/ Swin Transformer backbone (L.iu et al. 2021), MMRotate) to generated images for instance-level consistency.
Shape Fidelity. To assess the geometric quality of generated instances, we perform pair- wise comparisons with ground-truth shapes. Each instance pair is cropped, resized to 64 64, and converted to edge maps. We compute five metrics: IoU, Dice, Chamfer Dis- tance (CD), Hausdorff Distance (HD), and SSIM (Wang et al. 2004).
Downstream Utility. We train detector on mixed real and generated images and port mAP50. mAP75, and overall mAP on real test data using Oriented R-CNN (Swin backbone) with batch size of 24 on 8 8xNVIDIA 4090 GPUs.
4.2 QUALITATIVE RESULTS
Comparative Results. Figure compares the generation results of OF-Diff with other methods. OF- not only generates more realistic images but also has the best controllability. For instance, in the first two cases, OF-Diff successfully controlled the number and layout information of the generated objects. The third and fourth cases demonstrate the accuracy of OF-Diff generating small targets, which other algorithms fail to do accurately. The last case shows the superiority of OF-Diff over other algorithms when generating objects with complex shapes such as airplane.
Diversity Results. The images generated by OF-Diff consistently present plausible textures and re- alistic object shapes, as shown in Figure in Appendix. For instance, airplanes rendered at different orientations maintain coherent semantic relationships with their surrounding environments, Even in small-object scenes (some of which are grayscale remote-sensing images from the DOTA dataset), OF-Diff can still generate visually faithful and geometrically accurate results.

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Published as a conference paper at ICLR 2026
Label
LifoutDiffusion
GLIGEN
AcroGen
CC-Diff
OF-Diff (ours)
Real
$
Figure 4:
Qualitative results DIOR, DOTA and HRSC2016. OF-Diff is more realistic and fidelity compared to other methods.
Table I: Quantitative comparison with SOTA methods on DIOR and DOTA. We evaluate perfor- mance generation fidelity (FID, KID, CMMD), layout consistency (CAS, YOLOScore) and trainability (mAP). OF-Diff demonstrates superior overall performance.
DIOR Datasel
DOTA Datasel
FID
KIDJ
CMMDJ,
CAS1
YOLOScore
mAP1o
FID.
KIDJ
CMMDJ
CAS
YOLOScuref
mAP5e
Liy
37.60
0.015
0,447
70.32
7.01
52.14
21.73
0.015
0.288
77.56
21.43
66.75
GLIGEN
76.41
6.51
$1.27
39.79
0.357
76.19
15.58
AeroGen
53.37
CC-Diff
49.62
0.024
0.685
82.61
42.17
53.48
32.40
0.019
0.279
81.63
49.62
66.52
Ours
24.92
0.011
0,312
82.55
$8.99
54.44
20.84
0.014
0.271
83.79
55.68
67.89
4.3
QUANTITATIVE RESULTS
Generation Fidelity and Consistency. We compared OF-Diff with state-of-the-art generation methods in remote sensing, including layoutDiffusion (Zheng et al. 2023). GLIGEN (Li et al. 2023), AeroGen (Tang et al. 2025). and CC-Diff (Zhang et al. 2024). The performance of these methods is reported in Table I OFf achieved nearly the best performance both generation fi- delity metrics (FID, KID, CMMD) and layout consistency metrics, especially on the DOTA dataset. Additional results are available the appendix A.4 for the HRSC2016 dataset.
Trainability of Object Detection. Following the data enhancement protocol in (Chen et al. 202.3) we double the training samples using OF-Diff and assess detection results with the expanded dataset. As shown in Table 9 in Appendix A.7 OF-Diff performs the best on both DIOR and DOTA with mAP improved by 2.2% and 1.94% compared to beaseline,rspecttviee
Notably, the performance of several polymorphic and small object classes shows significant improvement. According to Figure (a) and (b), the APso increases by 8.3%, 7.7%, and 4.0% for airplane, ship, and vehicle on DIOR, and 7. %, 5.9% and 4.4% for swimming pool, small vehicle, and large vehicle DOTA.
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100
235
0.08
23.5
12.9
(4) Prnclle ym BHOR Dotret
DOTA Dotnet
10 Effect mn
Efflcr of PiD
Figure 5: AP DIOR and DOTA.
Table 2: Object-Shape Fidelity on Canny Edge Maps. We measure the morphological similarity between generated and ground-truth instances by computing loU, DICE, Chamfer Distance (CD), Hausdorff Distance (HD), and SSIM.
DIOR Dataset
DOTA Datasel
Method
loU't
Dice
CD4
HDL
SSIMT
lol't
Dicef
CDL
HDI
SSIMT
LiyouDDff
0.0497
0.0908
12.037
25.962
0.1667
0.0402
0.0748
15.229
30.202
0.2194
GLIGEN
0.055
0.1002
12:257
25.850
0.1652
0.0645
0.1182
10.432
23.196
0.1967
AemGen
0.0855
0.153
8.209
20.314
0.2142
0.0863
0.1536
8.1386
20.687
0.2261
CC-Diff'
0.0891
0.1582
8,0909
20.066
0.1963
0.0692
0.1255
9.6226
21.247
0.2171
Ours
0.1009
0.1763
7.6579
19.459
0.2691
0.1205
0.2045
6.6317
17.311
0.2938
Object-Shape Fidelity. We measure the morphological similarity between the generated instances and ground truth by calculating the Intersection over Union (IoU), DICE coefficient, Chamfer dis- tance (CD), Hausdorff distance (HD), and Structural Similarity Index (SSIM), based on the Canny Edge Map. As shown in Table 2 the results demonstrate that OF-Diff attains state-of-the-art per- formance in all evaluation metrics for object-shapp fidelity. Specifically, we first convert the rotated bounding box (R-Box) to a horizontal bounding box (H-Box) and crop the instance with 20% padding to ensure the full object captured. The cropped patches then resized 64 x 64 pixels, and their shapes are extracted using cv2.Canny. For a detailed qualitative comparison, Figure [T] in Appendix A.8 visualizes the instance patches and their corresponding edge maps from differ- ent methods Each image set is ordered as follows: Ground Truth, OF-Diff, AeroGen, CC-Diff, GLIGEN, and LayoutDiff, demonstrating our method's superior ability to adhere to object shapes.
Adaptability of Unknown Layout. To evaluate robustness of these methods, we also generate images based on the unknown layouts during the training phase. According to Table for unknown layout, OF-Diff performs well in terms of generation fidelity, layout consistency, and trainability. In downstream tasks, OF-Diff still delivers 1.54% mAP gain over the second-best method.
The Detailed Results of Downstream. Table 10 and 1 in Appendix A.8 report the average preci- sion (AP) obtained by the competing generative methods over multiple categories in the downstream tasks. From Tables 10 it can be observed that OF-Diff (ours) achieves clear advantage in several categories. For instance, OF-Diff achieves superior performance on Airplane (71.3%), Golf Field (75,4%) and Ship (70.5%), with improvements of approximately 5% 10% over the second-best method. For a few other categories, OF-Diff does not deliver the top AP, yet the gap to the best result remains marginal. Table shows that, on the DOTA dataset, OF-Diff obtains the highest AP in roughly half of all categories and still delivers notable gains in categories such as Small-vehicle (68.3%), Ship (84.4%) and Swimming-poot (67.9%).
4.4 ABLATION STUDY
We assessed the impact of different modules on image generation semantic consistency and down- stream trainability by OF-Diff Table We found that the images generated with captions are more in line with semantic consistency and human aesthetics, but the fidelity of these images de- creases. This is equivalent to the data distribution deviating from the real dataset and being more inclined towards the data distribution during traiinngn We conduct human/GPT assessments and
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Table 3: Quantitative comparison on the unknown layout dataset during training (DIOR Val),
Unknown Layout during Training
Method
KID
CMMDL
CASt
YOLO Scoret
mAP
mAPso't
mAPrs1
LiyouDDiff
44.58
0.018
0.539
29.34
10.37
30.41
53.07
32.07
GLIGEN
39.56
0.013
0.444
66.36
2.13
30.06
$2.68
31,29
ActoCien
28.62
0.013
0.276
80.78
46.36
32.98
55.11
34.26
CC-Dii
32.49
35.39
Ours
24.18
0.012
0.271
83.34
49.59
33.02
56.65
36.17
Table 4: Ablation study: impact of ESGM, Online-distillation Les and DDPO semantic consis- tency (CAS) and downstream trainability (YOLOScore and mAP50).
ESGM
Le
DDPO
FID
KID
CMMD
CAS f
YOLOScere
mAPso
42.59
0.029
0.965
80,27
41.20
52.13
24.87
0.012
0.428
82.16
55.08
$2.76
X
41.26
0.027
0.815
81.06
42.53
53.41
V
J
X
24.98
0.010
0.313
82,30
57.83
54.31
/
V
25.78
0.013
0.368
82.37
58.26
54.17
X
37.98
0.025
0.613
81,91
47.74
53.21
0.312
54.44
a fine- tne-grained feature analysis in Appendix A.7 which collectively reveal the nature of this trade- off. Therefore, the ablation experiments for each module were conducted based on the absence of caption input. The contribution of each module to the enhancement of image generation fidelity is evaluated by incorporating additional components into the diffusion model with online-distillation. DDPO indicates whether fine-tune the trained diffusion model through reinforcement learning. Results show that Enhanced Shape Generation Module (ESGM), Online-Distillatio (Lo) and the DDPO based KNN and KL Divergence effectively improve the performance metrics. Notably, ESGM can substantially improve the YOLOScore by over 10%. In addition, we vary the weighting coefficient À in the consistency loss (Eq. 7) to assess its impact mAP and FID. As shown in Figure (c) and (d), both metrics are optimal at À 1.
4.5 DISCUSSION
As shown in Figure 10 in Appendix A.3 the inclusion of additional captions as input has signifi- cant impact on the outcomes of image generation. Specifically, incorporating captions enhances the aesthetic appeal of the generated images, resulting in richer and more visually pleasing color com- positions. However, this improvement comes cost: similar to CC-Diff, it leads deviation of the generated data distribution from that of the original real data. In contrast, when no additional captions are provided as input, although the generated images may appear less aesthetically refined, their data distribution remains closer to that of real images. A user study from both human and GPT-5 in Table have confirmed this. Further analysis of the generated-image distribution and the impact of aesthetics performance is provided in the Appendix A.7
CONCLUSION
Existing image generation methods struggle to precisely generate dense small objects and those with complex shapes, such as numerous small vehicles and airplanes in remote sensing images. To address this, we introduce OF-fiff, an online-distillation controllable diffusion model with prior shapes extraction and DDPO. During the training phase, we extract the prior shape of objects to enhance controllability and use a online-distillationn diffusion with parameter sharing to improve the model's learning ability for real images. Therefore, in the sampling phase, OF-Diff can generate images with high fidelity without real images as references. Finally, we fine-tune the diffusion by DDPO that combines KNN and KL divergence to make the synthesized images more realistic and consistent. Extensive experiments demonstrate the effectiveness and superiority of OF-Diff in generating small and difficult objects with complex structures and dense scenes in remote sensing.

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ACKNOWLEDGEMENT
This work was supported in part by the National Natural Science Foundation of China under Grants 62271153 and 62506229, the Natural Science Foundation Shanghai under Grant 25ZR I 1402268, and the Shanghai Qi Yuan Innovation Foundation.
ETHICS STATEMENT
This work adheres to the ICLR Code of Ethics. Our study focuses on generating synthetic remote sensing images using diffusion models, and does involve human subjects or personally identi- fiable information. All datasets used are publicly available and appropriately licensed for research purposes. We have taken care avoid introducing harmful biases misrepresentations in the gen- erated images. While OF-Diff aims to improve object fidelity and controllability in synthetic data for downstream tasks such as object detection, we acknowledge that misuse of generated images could have unintended consequences. Researchers using this technology should ensure ethical and lawful application, and consider potential societal impacts. No conflicts of interest or undisclosed sponsorship influenced this work.
REPRODUCIBILITY STATEMENT
We have made efforts to ensure the reproducibility of our results. The main paper provides detailed descriptions of the OF-Diff architecture, online-distillation training strategy, and DDPO fine-tuning process. All datasets used are publicly available, and the data preprocessing and layout-to-image preparation steps are described the main and Appendix. Hyperparameters. training sched- ules, and evaluation metrics are fully documented in the supplementary materials. Additionally, we provide anonymized source code and configuration files facilitate replication of our experiments.
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A APPENDIX
A.I THE USE OF LARGE LANGUAGE MODELS (LLMs)
Large Language Models (LLMs) were used in this work exclusively for language polishing and improving the clarity of writing. No LLM was used for generating scientific ideas, experimental design, or data analysis. All technical content, results, and interpretations presented in this paper are solely the work of the authors. The authors take full responsibility for the content of this paper, including any text refined with the assistance of L.MMs.
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label
Result 1
Result 2
Real Image
Figure 6: The diversity of different results from the same OF-Diff model.
A.2 REINFORCEMENT LEARNING STRATEGY
The mapping relationship is defined as follows:
T(a [8)) (x.-1 Xx,
(10)
P(s+41 88,, az) 2 (des 8e-1, 8x
(11)
Po(8o) A (p(c), 8r. NN(,,) I))
(12)
R(st, ar) A (r(xo:c): if
(13)
00
otherwise.
where dg denotes the Dirac delta distribution whose probability density is zero everywhere except at z. The symbols St and represent the state and action at time t, respectively. Specifically, st is defined as the tuple composed of the condition c, the time step and the noisy image x, that time, whereas 0e is defined as the noisy image x,-1 from the preceding time step. The policy is denoted by T(a,) 8t), the transition kernel by P(8t+1 8t, at), the initial state distribution by (o())). and the reward function by R(st, az).
For detailed DDPO policy, we employ a ResNet101 pre-trained on ImageNet-IK as our feature ex- traction model, and utilize KNN and KL divergence to compute both the diversity among generated images and their similarity to real images. Let X denote the set of generated images, Y represent the real images, where E X, yo Y, and M is feature extraction model.
The KNN reward calculated as follows: 1) First, we extract features from X using model M: F. (X). 2) For each feature vector fi E Fz, we compute its K-nearest neighbors among all feature vectors få E F.. The KNN reward for 2; is the average of these nearest neighbor distances, denoted as KNN(F,, Fz). In implementation,
set K to 50.
The KL reward is calculated as follows: 1) We extract features from both X and Y using model M: F- M(X and F,, MY). For each feature vector f3 F, and f Fy. compute KL ft) for each i, and use KLLf fi) as the KL. reward for Z4.
In summary, the reward for a generated image x; is computed as:
A KNN F2) WKL(fi,
(14)
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Real
Label
Mask
Generation
Training with samples under heavy occlusion:
FID:79.26
Training without samples under heavy occlusion:
FID: 77.91
Figure 7: The results of OF-Diff in handling objects under heavy occlusion. The results indicate that while severe occlusion does indeed cause a certain degree of degradation in the quality of the target mask extracted by ESGM, it has little impact on the generated quality FID.
A.3 ANALYSIS AND DISCUSSION
According the current experimental results, adding the DDPO strategy does not simultaneously outperform previous results on all metrics. Using reinforcement learning strategies can indeed im- prove the performance of downstream tasks, but does not necessarily improve the quality image generation simultaneously. In other words, reinforcement learning strategies can also purposefully improve the quality of image generation, but this may come the cost not improving the perfor- mance of downstream tasks.
The proposed OF-Diff injects object shape masks extracted from the image layout as controllable conditions into the diffusion model, which effectively enhances object fidelity and improves the generation of small objects. However, this also makes the model dependent on the quality of the extracted shape masks. We analyze the impact of distorted mask on the model's generated re- sults. Specifically, we selected cases such as objects under heavy occlusion to examine the model's generation performance. Based the analysis results in F found that even under severe occlusion conditions, ESGM still demonstrates strong object mask extraction and generation capa- bilities. However, when the generated mask shape exhibits certain anomalies, it does produce objects matching that distorted shape. Nevertheless, this does not affect the overall FID and trainability of the generated images. Although the shapes currently extract may exhibit edge anomalies in the object mask due to occlusion and other issues, complete errors are extremely rare.
A.4 QUANTITATIVE RESULTS ON HRSC2016 DATASET
Table reports the comparative results HRSC2016. where method consistently achieves strong performance. Although it ranks second on CMMD, CAS, and YOLOScore- which mainly reflect aesthetic quality or local recognizability--i it attains the best results FID and KID, which measure distribution fidelity, as well as on the most crucial downstream metric, mAP50, outper- forming the second-best method by +1.5%. This indicates that our generated data preserves the real remote-sensing distribution more faithfully and thus provides more effective support for downstream tasks. A more detailed analysis provided in Appendix A.7
Table 5: Fidelity and Downstream Performance on HRSC2016
HRSC216 Dalasel
Method
FIDJ
KIDJ
CMMDJ
CAS
YOLOScoref
mAPso 1
LayouDDiff
120.68
0.152
1.763
24.51
2.51
56.97
GLIGEN
92.92
0.037
0.634
35.41
5.03
39.72
AcroGen
97.44
0.055
39.62
16.4
47.68
CC-Diff
84.55
0.035
0.681
45.27
32.42
62.57
42.19
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Table 6: The impact of real and generated images different ratios on mAP for downstream tasks.
Data Composition
mAP (%)
100% Generated
45.67 (-7.17)
50% Real + 50% Generated
50.74 (-2.10)
100% Real
52.84
100% Real + 50% Generated
53.92 (+1.08)
100% Real + 100% Generated
54.38 (+1.54)
100% Real + 200% Generated
54.74 (+1.90)
100% Real + 300% Generated
54.82 (+1.98)
Table 7: The data on the computational cost of training OF-Diff compared to the key baselines.
Train GPU Mean Memory (MB)
Train GPU Hours
Inference Mean Time/Sample (s)
Models
LiyoutDiff
29232
41.33
<1s
GLIGEN
14186
57.76
5.18
AeroGen
27634
49.52
1,85
CC-Diff
13668
38.01
3,96
OF-Diff
27340
44.27
3.42
A.5 THE MAP EVOLUTION GIVEN DIFFERENT AMOUNTS OF SYNTHETIC AND REAL DATA
We conduct multiple experiments on trainability using different quantities of real and generated data. The results shown in the T Experimental results indicate that using only 100% synthetic data struggles to achieve downstream task performance comparable to real data. However, this also demonstrates that even without single real image, relying solely on synthetic images object detection algorithms to achieve a mAP of 45.67%. Furthermore, training with a larger volume of generated images can effectively enhance the model's object detection capabilities. However, when the amount of generated data reaches three times that of real data (based on the generation setting described in the paper), downstream performance shows little further improvement.
enable
A.6 THE COMPUTATIONAL COST
We provide the data the computational cost of training OF-Diff compared to the key baselines in the Table Experimental results indicate that although OF-Diff not the most optimal in terms of training costs (GPU memory and GPU hours) and inference time, it remains nearly the second-best among these methods and does not incur high computational costs.
A.7 DESTHETIC-DOWNNSTRERR PERFORMANCE CONELICT
To further reveal the potential conflict between aesthetic quality and downstream task performance, conduct three-part analysis consisting of questionnaire evaluation, downstream performance comparison, and feature-level visualization.
(1) Human/GPT questionnaire study. As shown in Fig. g design two targeted questions:
QI. Which image more closely matches the style of real remoot-sensing imagery? (e.g., realistic noise patterns, texture details, natural illumination, authentic object boundaries)
Q2. Which image looks more aesthetically pleasing? (e.g., clarity, color harmony, contrast, smooth- ness, visual comfort, overall appearance)
We invite PhD researchers and 8 remote-sensing experts to participate, and additionally perform 3 rounds of GPT-5 evaluation. For each class in DIOR, randomly sample one pair of images generated with vs. without captions (from the same ground truth), resulting in 20 image pairs Each pair is randomly shuffled to avoid positional bias. The results are shown in Table g each
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"You are a strict Image evaluation assistant."
"Your entire reply MUST be valid ISON object with keys 'ql
'az"
"and
either "I" '2", extra "Output 150N: (V"qIV": \ or 2X', V42V": 111 or 2V")"),
User prompt:
"You
given Images.
inage is imago
1. The SECOND inage is inage 2. A.Anndnswer the following two
o1 Which dlssly style sersing
questions about these inages. AnFor each question,
must inage 2.Anetuunn your answer valid 350N object with this fornat:An( (n"""1"""""" ar 2", """""""""" ""JnVvrusstions:W Which image nore closely natches the sensing Heegryy\n(ee... realistic patterns, texture details, natural illumination, authentic objact houndarios) vnlnssor ONLY with or vng:: which image aesthetically pleasing you?\n(e-gus clarity, color harnony, contrast, smoothness, visual confort, overall /nknswer Lnknssee ONLY with or 2.10"
deg- rallet pottern, tecure crsilt, naturallia matinn authonts eq
choose
inage
style
-32 Whitt auchediclly pleasng your
(eg. cafty or hammony, ec cormatt smoothess, wasi conton, overl
appeaemnct
appaaraacc))
(b)
Figure 8: Aesthetic evaluation questionnaire design for generated images: (a) human experts, (b) GPT-5.
value represents the average frequency with which the corresponding option was selected across all questionnaires.
Table 8: Single-choice results from human experts and GPT-5 (averaged multiple annotators repeated evaluations).
Human experts
GPT-5
Option
Q1
Q2
Q1
Q2
w./ caption
6.57
11.21
2.33
15.33
w./o. caption
13.43
8.79
17.67
4.67
Both human experts and GPT consistently prefer the caption-conditioned images in terms of aesthet- ics, but find them less similar to real remote-sensing imagery. contrast, images generated without captions appear less visually appealing but better preserve the real-world texture and structural char- acteristics needed for downstream tasks.
(2) Downstream performance comparison. On the DIOR dataset, as shown in the table at the bottom-right of Fig. 9 adding captions reduces the downstream improvement AmAP50 by 1.15 and also leads to significantly higher FID. Combined with finding (1), this reveals that caption- guided generation tends to over- bvautbe images-masking masking the natural imperfections of remote- sensing imagery-aae and consequently harms downstream performance.
We observe that adding captions produces outliers, whereas samples generated without captions align more closely with the GT distribution, indicating higher fidelity.
(3) Feature-level visualization. We also visualize features using t-SNE in Fig. 9
Taken together, these findings suggest that models should remain faithful to the inherent quirks and imperfections of the original remote-sensing data, rather than generating overly "idealized" or aesthetically enhanced imagery. Incorporating captions risks amplifying the latter behavior.
Additional examples are provided in Fig. 10
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t-SNE Visualization of Image Features
Samples Generated Captions
Samples Gencrated w.o. Captions
Real samples
O: Outher Samples
H-SNE Dimension
Caption
AmAPso
AFid
w./o.
1.47
29,92
w./
0.52
42.17
S-NE Dimension
Figure 9: t-SNE feature analysis generated samples w./ and w./o. captions. Incorporating cap- tions produces large number of outlier samples, lowers fidelity (higher FID), and further degrades downstream performance (lower s.))
More faithful real images without caption
More aligned with human aesthetics with caption
Figure 10: The influence of caption on the generation of images in terms of being more realistic and more aesthetically pleasing.
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A.8
MORE QUALITATIVE AND QUANTITATIVE RESULTS
DIOR
Airplane
Chimney
GroundTrackkield
Storagetank
Airport
Dam
Harbor
Tenniscourt
BaseballField
Expressway-Servins Service-Area
Overpass
Trainstation
BasketballC ourt
Expressway-Tol--Sene Toll-stttoooo
Ship
Vehicle
Bridge
Golffield
Stadium
Windmill
D
DOTA Harbor
Storagetank
Bridge
o
Boseball-Diamone
Swimming-Poo Pool
Fround-Track-FFiel
Helicopter
Roundabout
Tennis-Court
Large- Vehicle
Plane
Soccer-Bar Ball-Field
Vehicle
Ship
Basketball-Court
Figure 11: Comparison of generated instance patches and their Canny edge maps for the same bbox on the DIOR and DOTA dataset. Each image set is ordered follows: Ground Truth, OF-Diff, AeroGen, CC-Diff, GLIGEN, and LayoutDiff.
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Table 9: Trainability (t) comparison on DIOR and DOTA. 'Baseline' denotes accuracy with the
unaugmented dataset.
DIOR Dataset
DOTA Dataset
Method
mAP
mA
mAP75
mAP
mAPso
mAP75
Baseline
30.51
52.84
32.10
38.09
66.31
38.44
LayoutDiff
29.81
52.14
30.36
38.91
66.75
40.37
GLIGEN
28.48
51.27
29.21
38.84
66.10
40.24
AeroGen
31,53
53.37
33.60
38.45
67.09
39.07
CC-Diff
31.82
53.48
33.97
38.51
66.52
39.02
Ours
32.71
54.44
34.05
40.03
42.20
Table 10: Detailed downstream trainability results (measured by average precision) on the DIOR
dataset.
DIOR Dataset
Method
Expressway Service-area
Expressway toll-station
Airplane
Airport
Baseball field
Baskethall
Bridge
Chimney
Dam
Golf
court
Field
LayoutDiff
53.1
44.8
62.8
29.4
63.2
79.6
25.9
72.6
22.4
69.3
GLIGEN
52.7
44.8
62.6
26.7
63.0
79.6
25.2
72.6
19.5
67.4
AcroGen
58.1
45.2
63.1
32.7
63.4
81.0
29.5
72.6
21.1
69.1
CC-Diff
$3.5
45.1
62.9
38.4
63.3
79.9
29.3
72.7
27.6
70.5
Ours
58.0
44.9
71.3
37.0
63.2
80.2
30.1
72.5
24.9
75.4
DIOR Dataset
Method
Ground Track-field
Harbor
Overpass
Ship
Stadium
Storage Tank
Tennis
Trainstation
Vehicle
Windmill
Court
LayouDDiff
71.2
32.8
43.9
62.9
59.0
52.5
72.4
$2.1
26.9
46.0
GLIGEN
70.1
30.3
45.8
62.8
56.8
52.0
72.5
49.0
26.9
45.3
AemGen
71.0
42.7
50.7
62.9
56.6
44.5
72.5
$2.6
31,4
46.7
CC-Diff
64.6
43.1
49.0
63.0
61.7
44.7
72.4
54.4
27.0
46.5
Ours
43,9
49.4
70.5
52.7
44.4
72.4
54.1
31.0
46.7
Table 11: Detailed downstream trainability results (measured by average precision) on the DOTA dataset.
DOTA Dataset
Method
Ground Track-field
Plane
inseball-diamon Bridge
Small-vehicle
Large-vehicle
Ship
Tennis-court
LayouDDiff
80.4
74,2
48.8
59.9
62.9
72.7
82.5
89.6
GLIGEN
87.0
72.9
47.3
56.4
63.7
73.1
82.7
90.1
AcroGen
86.1
77.3
48.6
58.6
64.5
78.1
82.5
83.3
CC-Diff
87.2
73.4
47.1
57.8
64.3
73.9
82.6
89.2
Ours
85.2
75.3
46.5
60.4
68.3
77.2
84.4
90.4
DOTA Dataset
Method
Basketball-court
Storage-tank Soccer-ball-nee Roundahout
Harbor
Swimming- pool
Helicopter
LayouDDiff
78.9
76.3
46.3
47.6
60.9
62.1
$7.9
GLIGEN
79.6
76.5
43.1
AcroGen
77.3
79.9
44.8
46.6
59.4
62.6
56.4
CC-Diff
79.0
82.7
42.7
43.1
58.9
62.7
52.9
Ours
83.3
77.1
42.1
44.7
62.1
67.9
53.3
20

---

Published as a conference paper at ICLR 2026
Label
Liffuonitfuusion
GLIGEN
AcroGen
CC-Diff
OF Diff (ours)
Real Image
12
ANI
Figure 12: Additional qualitative results DIOR and DOTA. The results demonstrate that OF-Diff has certain superiority and accuracy generating small objects, and also has an advantage in generating the shapes of objects. For instance, the aircraft target in the third row is generated more accurately by OF-Diff, with more realistic structure. The small vehicles in the fourth and fifth rows and the large vehicle in the sixth row also more accurately generated. Additionally, the small ship in the seventh row is generated with greater accuracy.
21


Reviews:
[{"review_id": "Prf4Qvcajh", "reviewer": "Reviewer_3Som", "summary": "This paper introduces Object Fidelity Diffusion (OF-Diff), a novel layout-to-image diffusion model designed specifically for generating high-fidelity remote sensing (RS) imagery. The primary motivation is to address critical failure modes in existing methods, such as control leakage, structural distortion, and dense generation collapse, which limit their utility for downstream tasks like object detection.", "questions": "Robustness of ESGM: The quality of the generated images seems highly dependent on the quality of the shape masks produced by ESGM. How does OF-Diff perform when ESGM fails or produces a distorted mask (e.g., for objects with complex boundaries or under heavy occlusion)? Is there a mechanism to handle such failures, or does the model simply replicate the distorted shape?\n\nOn the DDPO Reward Function: The KNN reward term encourages diversity by pushing generated samples away from the nearest neighbors in the real dataset. Could this potentially penalize the generation of \"typical\" or common instances and favor only rare or outlier-like objects? How was the balance between the KNN and KL terms (controlled by $\\omega$) determined to prevent this?\n\nInference Speed and Cost: Could you provide details on the inference speed of OF-Diff compared to other methods? Specifically, since ESGM is only used to populate a mask pool for inference, how large does this pool need to be for good performance, and does the selection from this pool add any significant overhead?\n\nRegarding the use of captions (Section 4.5): You note that including captions improves aesthetic appeal but harms downstream performance by deviating from the real data distribution. This is a very interesting finding. Does this imply that for data augmentation purposes, it is better to have models that are \"faithful\" to the original dataset's quirks and potential imperfections rather than models that generate more \"idealized\" or aesthetically pleasing images? I would appreciate it if you could elaborate on this insight.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. ESGM: Leverages pre-trained vision-language and segmentation models (RemoteCLIP and RemoteSAM) to extract precise object shape masks, providing strong geometric priors beyond simple bounding boxes.\n\n2. Employs a teacher-student architecture where a \"teacher\" decoder (conditioned on both image and shape features) guides a \"student\" decoder (conditioned only on shape features). This allows the model to learn to generate high-fidelity textures and details without requiring real image references during inference.\n\n3. Applies Denoising Diffusion Policy Optimization (DDPO) as a post-training step, using a reward function based on KNN distance and KL divergence to enhance the diversity and distributional consistency of the generated images.\n\nComprehensive experiments on the DIOR and DOTA datasets demonstrate that OF-Diff outperforms state-of-the-art methods in generation fidelity, layout consistency, and its utility in improving downstream object detection performance.", "weaknesses": "1. The ESGM module is critically dependent on two large, specialized models: RemoteCLIP and RemoteSAM. While effective, this raises questions about the framework's scalability, accessibility, and potential biases inherited from these foundational models. The paper could benefit from a discussion on the computational cost of this \"template extraction\" phase and an analysis of how errors from ESGM might propagate through the diffusion pipeline.\n\n2. The paper clearly defines the DDPO reward function (Equation 9) but omits crucial implementation details for the KNN component. As the authors know, computing KNN in the high-dimensional pixel space is infeasible and perceptually meaningless. It is standard practice to compute this in a low-dimensional embedding space (e.g., using a CLIP or VAE encoder). While this is likely what the authors did, this critical detail should be explicitly stated in the implementation section to ensure reproducibility and clarity. Mentioning the specific pre-trained encoder used would be essential.\n\n3. The paper exclusively focuses on object detection as the downstream task. While this is a highly relevant application, remote sensing involves many other perception tasks, such as semantic segmentation and change detection. Demonstrating the utility of OF-Diff for these other tasks could further strengthen the paper's claims of general applicability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9sEkyYYBtA", "reviewer": "Reviewer_HT6t", "summary": "This paper proposes Object Fidelity Diffusion (OF-Diff), a novel diffusion-based model for layout-to-image generation in remote sensing (RS). The key idea is to improve object fidelity and layout controllability without relying on real-image references at inference time. The authors introduce:\n1. Enhanced Shape Generation Module (ESGM) to extract object shape priors from bounding box layouts.\n2. Online distillation to align shape-based generation with real-image features during training.\n3. DDPO (Denoising Diffusion Policy Optimization) to fine-tune the model for better diversity and semantic consistency.\nThe model is evaluated on DIOR-R and DOTA datasets using a comprehensive set of metrics including FID, KID, YOLOScore, and downstream detection mAP. Results show superior fidelity, layout consistency, and downstream utility, especially for small and polymorphic objects.", "questions": "see weaknesses.", "limitations": "", "rating": 6, "confidence": 2, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. OF-Diff does not require real-image references at inference, a significant practical improvement.\n2. State-of-the-art results on both DIOR-R and DOTA datasets, with mAP improvements of up to 8.3% on airplane and 7.7% on ship categories.\n3. The paper is well-structured, with clear problem motivation, method description, and experimental analysis.", "weaknesses": "1. The online distillation and DDPO fine-tuning steps are computationally expensive, but the paper does not report training time, GPU usage, or memory overhead.\n2. The paper shows that adding captions improves aesthetics but hurts fidelity (Fig. 7). However, this trade-off is not deeply analyzed. A user study or perceptual evaluation would help clarify when and why to use captions.\n3. The method heavily relies on ESGM-generated shape masks. While the paper mentions that distorted masks lead to poor generation, it does not quantify how robust the model is to noisy or incomplete masks?\n4.The model is only evaluated on two datasets (DIOR-R and DOTA), both of which are airborne/satellite optical imagery.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "CGcUf2IzFb", "reviewer": "Reviewer_8kyc", "summary": "This work proposes a a method for generating satellite images conditioned on object layouts. Motivation for this work is the need of augmenting existing training datasets, thus, proposing a method for generating additional label-image pairs. Furthermore, this work focuses specifically on instance/object level generations (instead of the commonly adopted semantic maps conditioning or text conditioning).", "questions": "- Given a baseline model trained on DIOR/DOTA default dataset, authors show downstream improvements when baseline model is trained with original + OF-Diff generations (Figure 5). It would be interesting to see the AP evolution given different amounts of synthetic vs real data. For instance:\n   - Train a baseline model on 100% real images\n   - Train a baseline model on 100% generated images\n   - Train a baseline model on 50% real + 50% generated images\n   - etc.\n   - Train a baseline model on 100% real + 50% generated images\n   - Train a baseline model on 100% real + 100% generated images\n   - Train a baseline model on 100% real + 200% generated images\n   - etc.\n- Do authors have any intuition when the baseline model performance plateaus? In other words, the point at which generating more images will not improve downstream performance?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "- Authors identify a lack in current literature: few works sucessfully tackle instance-level generation given the difficulty of the task. Instance-level (layout-to-image paradigm) gives more precise control over the generations and alignment with the ground truth conditions.\n- Authors propose a realiable pipeline for achieving high layout fidelity generations by DDPO finetuning, and without the need of using real control images.\n- Authors provide ablation studies for the design decisions.\n- Extensive evaluation is carried out.\n- Qualitative results look strong compared to other models.", "weaknesses": "Authors do not provide any dataset augmentation experiment for OOD-datasets. Such experiment would be useful to prove the usefulness of the model beyond their training dataset distribution, to see if their generations are actually useful for other downstream datasets. I believe this is an important experiment that should be carried out, as it determines the overall usefulness of the generated images not just within the training distribution.\n\nI suggest authors to select some other dataset (not DOTA or DIOR) and compare a baseline model trained on the original dataset and an augmented version of the target dataset.\n\nIn fact, Tables 6 and 7 show very small downstream improvements in terms of trainability when compared with other methods. Could authors provide some intuition why generations are not always profitable for training? Trainability is an important part of the work carried out. Authors could provide more ablation experiments showing whether the generated images are usefulness for training.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "5MAJfBn6PW", "reviewer": "Reviewer_7tpr", "summary": "This paper proposes an object fidelity diffusion method, which extends the layout-to-image paradigm into the remote sensing sense. This method combines many existing modules to achieve controllable image generation.", "questions": "1. Can you precisely state the novel algorithmic contribution of OF-Diff, distinct from the existing components (ControlNet, distillation, DDPO) it builds upon?\n2. Have you attempted to fine-tune the CC-Diff baseline on your specific datasets to ensure a fair comparison? Could its distribution shift be mitigated?\n3. How does the performance of OF-Diff degrade when the input shape masks from ESGM are noisy or partially incorrect? Please provide a robustness analysis.\n4. Can you provide data on the computational cost (e.g., GPU hours, memory usage) of training OF-Diff compared to the key baselines?", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. This is a layout-to-image generation method for the remote sensing field.\n2. The proposed method integrates an online-distillation strategy and DDPO fine-tuning to achieve high-fidelity image generation.", "weaknesses": "1. The proposed OF-Diff is primarily an assembly of existing, well-known components: ControlNet for conditioning, a form of online distillation for feature alignment, and DDPO for fine-tuning. \n2. The central claim of \"reducing reliance on real images\" is misleading; the method heavily depends on real images during training to extract shape masks via RemoteSAM and to train the teacher model in the distillation process. The \"prior shape extraction\" is essentially semantic segmentation, which is a standard technique. The paper fails to convincingly demonstrate what the key, novel insight is, beyond a specific combination of these existing blocks. \n3. The comparison methods discussed in the paper (such as LayoutDiffusion and GLIGEN) were not specifically designed for remote sensing imagery. Applying them directly to remote sensing scenarios and using them as baselines is unfair. Furthermore, the authors did not provide sufficiently detailed training configurations (such as whether all comparison methods underwent adequate adaptation to remote sensing data), which may have led to biased comparison results.\n4. Although the paper demonstrates improvements in mAP for object detection tasks, it does not explicitly prove whether these gains stem from enhanced image quality or merely increased data volume. There is a lack of quantitative analysis regarding the actual contribution of generated images during detector training, such as through visualization or feature distribution alignment analysis.\n5. The entire pipeline is critically dependent on the quality of masks produced by the ESGM, which itself relies on external models (RemoteCLIP, RemoteSAM). The paper provides no analysis of what happens when this module fails or produces noisy/incorrect masks, which is inevitable in practice. How robust is OF-Diff to errors in the initial shape prior? If the extracted mask is distorted, will the generation process fail catastrophically? This is a major point of practical vulnerability that is completely unaddressed.\n6. The proposed system is exceptionally complex, involving multiple stages: ESGM, a dual-branch diffusion model with online distillation, and a subsequent DDPO fine-tuning step. The computational cost, memory footprint, and training time must be enormous compared to baselines like AeroGen. The paper completely omits any discussion of efficiency, training time, or inference speed. The practical utility is questionable if the method is an order of magnitude more expensive to train and deploy.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)