CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing
Authors: Xinran Qin
Zhixin Wang
Fan Li
Haoyu Chen
Renjing Pei
Wenbo Li
Xiaochun Cao
Venue: NeurIPS 2025 poster ()
Keywords: Photorealistic Image Editing
Camera Parameter Control
Diffusion model
Primary area: applications

Abstract: Recent advances in diffusion models have substantially improved text-driven image editing. However, existing frameworks based on discrete textual tokens struggle to support continuous control over camera parameters and smooth transitions in visual effects. These limitations hinder their applications to realistic, camera-aware, and fine-grained editing tasks. In this paper, we present CamEdit, a diffusion-based framework for photorealistic image editing that enables continuous and semantically meaningful manipulation of common camera parameters such as aperture and shutter speed. CamEdit incorporates a continuous parameter prompting mechanism and a parameter-aware modulation module that guides the model in smoothly adjusting focal plane, aperture, and shutter speed, reflecting the effects of varying camera settings within the diffusion process. To support supervised learning in this setting, we introduce CamEdit50K, a dataset specifically designed for photorealistic image editing with continuous camera parameter settings. It contains over 50k image pairs  combining real and synthetic data with dense camera parameter variations across diverse scenes. Extensive experiments demonstrate that CamEdit enables flexible, consistent, and high-fidelity image editing, achieving state-of-the-art performance in camera-aware visual manipulation and fine-grained photographic control.

CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing
Xinran Qin'; Zhixin Wang"; Fan Li?,
HaoYu Chen', RenJing Pei?, WenBo Li XiaoChun Cao
Shenzhen Campus of Sun Yat-sen University
Huawei Noah's s Ark Lab HKUST (Guangzhou) 4CUHK
"Adjust the image with focal plane 0./0757
"Adjust the image with focal plane 0.5/0.9"
"Adjust the image with aperture f/3 f77"
"Adjust the image with shutter speed 0.2 0.57
"Adjust the image with aperture f/eps"
Input
1/2
f/2.8
f/5.6
f/8
Figure 1: The proposed CamEdit enables photorealistic image editing through manual textual input of continuous camera parameters, including aperture, focal plane, and shutter speed, resulting in visually realistic outcomes.
Abstract
Recent advances in diffusion models have substantially improved text-driven image editing. However, existing frameworks based on discrete textual tokens struggle support continuous control over camera parameters and smooth transitions in visual effects. These limitations hinder their applications to realistic, camera-aware, and tne-ggriined editing tasks. In this paper, we present CamEdit, diffusion- based framework for photorealistic image editing that enables continuous and semantically meaningful manipulation of common camera parameters such as aper- ture and shutter speed. CamEdit incorporates continuous parameter prompting mechanism and a parameter- aware modulation module that guides the model smoothly adjusting focal plane, aperture, and shutter speed, reflecting the effects of varying camera settings within the diffusion process. To support supervised learning in this setting, we introduce CamEdit5OK, dataset specifically designed for photorealistic image editing with continuous camera parameter settings. contains over 50k image pairs combining real and synthetic data with dense camera
Equal Contribution
f Corresponding Author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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parameter variations across diverse scenes. Extensive experiments demonstrate that CamEdit enables flexible, consistent, and high-fidelity image editing, achieving state-of-the-art performance in camera-aware visual manipulation and fine-grained photographic control.
Introduction
52 54 51 32] have become powerful tools for both image generation and editing. They usually apply a pre-trained text encoder such as CLIP [48] and T5 [66] to inject manual textual prompts information into the generation process, enabling better generation quality and more precise control. Meanwhile, as social media platforms grow and smartphone cameras continue to advance, editing images to reflect photorealistic optical effects has
Recently, diffusion models [ 20 49 50
become practically valuable. This highlights the need for editing methods that can directly manipulate
camera parameters. However, most existing image editing methods 4 5 [9] 23 24
29 33 58 62
focus mainly on three main tasks: semantic editing, stylistic editing and structural editing.
Few prior works target photorcalistic image editing, which edits indistinguishable from real pho- tographs through precise control of camera parameters. In this work, we focus on the precise adjustments of focal pland", aperture and shutter speed in camera parameters, which play funda- mental role respectively in determining focal range, background defocus degree, and exposure time during the photo-taking process.
46 57
Diffusion models capture strong spatial priors and scene geometry [55], making them suited for photorealistic editing. Recent works encode camera settings as discrete tokens within text-to- image (T2I) or text-to-video (T2V) generation frameworks [TT] 70]. However, such discrete textual token-based approaches are difficult to directly apply to editing tasks involving continuous camera parameter control through textual prompt input (c.g. "Adjust the image with aperture f f/2.8", etc.). This mismatch hampers smooth parameter adjustment and limits applicability to photographic editing.
To overcome these challenges, we introduce CdmEdit, a diffusion baaded framework for photorealistic image editing that allows continuous control of camera settings using text prompts. Instead of turning parameter values into separate tokens, we propose a continuous parameter prompting method, which interpolates between predefined anchor embeddings in the text space. This preserves alignment with representation distribution the pre-trained model while enabling fine-grained control over a wide range of settings (such as "aperture f/[2, 10]", "shutter speed [0, 1)"). As diffusion backbones lack explicit camera priors and fail to capture parameter-specifer effects, we further propose a parameter- aware modulation module that conditions spatial and channel features throughout the diffusion transformer, making explicit both local and global effects that text embeddings alone miss.
Given the lack of high-quality datasets for photorealistic camera-aware editing, we construct hybrid dataset named CamEdit50K, which includes real- wolld photographs with extracted or estimated EXIF metadata") along with synthetic image pairs rendered under controlled variations in focal plane, aperture, and shutter speed. This dataset provides a strong foundation for learning models that are physically consistent and aware of the effect of varying camera parameters.
In summary, main contributions be summarized follows:
We propose CamEdit, a diffusion-basee framework for photorealistic image editing that en- ables continuous and fine-grained control over intrinsic camera parameters such aperture, focal plane, and shutter speed, entirely through manual textual prompts.
We design a continuous parameter prompting mechanism and a parameter-aware modulation module to enable smooth and physically consistent control across varying camera settings.
We construct dataset, CamEdit50K, which contains aligned image pairs and corresponding camera parameter instructions. addressing the lack of supervised data for photorcalistic image editing with continuous camera parameter.
I Focal plane is corresponding to the focal point in camera settings. EXIF is metadata embedded in image files that records camera settings such aperture and shutter speed.

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2 Related Work
Image Editing with Diffusion Models. Recent diffusiin-based generative methods such as Im- agen [54] and DALLE [49 leverage diffusion models conditioned on manual textual prompts to control the generation process. Consequently, Textual Inversion [14] and DreamBooth [53] allow for personalized image generation base on diffusion models. ControlNet [71] adds more control
by using conditions like depth, edges, or pose. LoRA-based techniques [22 34 35 update only
small part of the model for quick adaptation. Unlike image generation, image editing modifies the style, structure, or content of an existing image to achieve specific goals. Prompt-to-Pronee -o-pptppt [19] manipulates cross-attentin
between source and target prompts to guide the editing process. while InstructPix2Pix (IP2P) [5] extends this paradigm by fine-tuning diffusion models on synthetic triplets of (image, instruction, target). More recent approaches, such as InstructDiffusion [77 and MGIE [3] unify instruction-driven editing across a broad range of tasks and datasets, advancing general-purpose visual manipulation. To improve spatial fidelity, follow-up works incorporate localization priors to better preserve background consistency. Other efforts explore multi-task instruction tuning on large-scale synthetic datasets 337 69 64], enabling
such as masks and bounding boxes [58 24
finer-grained semantic control. Approaches with sliders [6 15 enable continuous control over the
attributes of image edits. Despite these advances, existing frameworks remain centered on semantic and stylistic edits, which are rarely considering the growing needs for photorealistic editing.
Camera- Awaree Models. Traditional camera-aware editing methods have shown strong performance in tasks such focal plane adjustment [57
45 46 63] and aperture simulation (766).. These methods
are typically based on physically inspired image formation models and often require additional inputs
such as depth maps aperture geometry (57
45 46 18, 61 [3) However, their applicability is
generally limited to single-purpose purpose editing scenarios due to their dependence on auxiliary data and restrictive modeling assumptions.
Recent diffusion-based approaches introduce camera control into generative pipelines, mainly focus- ing on extrinsic parameters like pose, viewing angle 28 [3), or motion trajectory in text-to-video generation [40] 66] 68). Conditioning is commonly achieved via camera tokens or scene descriptions to enable view synthesis and motion control. Some methods embed camera parameters as discrete tokens into T2I
I1 12 and T2V 770 diffusion models to generate images with varying physical properties. However, these approaches face two main limitations: they do not support editing real images in a physically consistent manner, and they represent continuous camera parameters in a discretized form, which restricts control precision and limits generalization.
3 CamEdit50K Dataset
Existing camera- datasets 56 70 7 mainly focus on generation tasks, often lacking aligned image pairs or sufficient variation in camera parameters and content diversity. To address these limitations, we introduce CamEdi550K, dataset specifically designed for photorealistic image editing under continuous, physically grounded camera control.
As shown in Table | and Figure CamEdit50K unifies paired real and synthetic imagery, multi- parameter coverage, and explicit camera settings to support camera-aware editing and evaluation. Real photos supply rich content but often lack complete metadata. We recover missing parameters through real-data parameter estimation pipeline. Synthetic images are produced with a synth- data rendering pipeline and come with ground-truth camera values, which enables accurate and dense supervision. By integrating these complementary sources, CamEdit50K delivers diversity and controllability, enabling continuous caanerr-parameter editing.
Real-Data Param Estimation. For the majority without metadata, we estimate these parameters using physically grounded methods: (i) Focal Plane: To ensure consistency across images, we define the focal plane within a normalized depth range [0, 1], representing focus from far to near. Depth maps are predicted by Depth Anything V2 [67] and normalized accordingly. The in-focus region identified by comparing the target image with an all-in-focus input [60], and its mean depth is used as the estimated focal plane. (ii) Aperiure: Since aperture primarily governs background defocus, we measure blur using an edge-based estimation [27]. The estimated defocus level is then converted to an effective aperture diameter via simplified thin-lens model [44] (ii) Shutter Speed: We estimate
3

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Focal Plane
Aperture
Shutter Speed
Focal plane
Aperture
Shutter Speed
ee [0.3]
PE (2,10)
pe [0.1]
pe [0:1)
y (2,10)
pe
Syath-Date
focus depth
PSF redius
EV
samaling
Focal plane
2500
23000
Real-Data Param Estimation
Synth-Data marth-Data Rendering
Aperture
2000
Shutter Speed
Num, pairs
Real-Data (No EXIF)
Parameters
Parameters Paired Synth-Data
Figure 2: Overview of the CamEdit50K construction pipeline across focal plane, aperture, and shutter speed. Real image pairs without EXIF metadata are parameterized using physical cues such focus depth, PSF radius, and exposure value. Synthetic pairs are generated by sampling camera settings and rendering photorealistic images under controlled conditions. The chart on the right shows the number images for each parameter in CamEdit50K.
Table 1: Comparison with existing camera aamera-aware datasets.
Dataset
Venue
SSamlles
Task
Real-Data
Synth-Duta Synth-Realism Parameter-Dense Scene Diversity
RealBokeh
Camera20k
SA 2025
20k
Generate
X
PhotoGien
70
CVPR 2025
3k 3
Generate
x
7
x
x
CamEdit50K
54k
Edit
/
exposure time from global brightness statistics and invert the camera response using a differentiable ISP pipeline
9 31 36), yielding shutter speed consistent with the observed luminance.
Synth-Data Rendering. We generate synthetic image pairs by sampling camera parameters and rendering the corresponding effects. (i) Focal Plane: We sample the focal plane from the interval [0, 1], selecting depths ranging from background to foreground. To simulate realistic depth-of-fiee effects, we employ differentiable bokeh renderer [57]. (ii) Aperture: Using the depth map from [67], we fix the focal plane on the foregroumd and apply BRIA. AI [3] matting to preserve sharpness in the focused region. A thin-lens renderer [45] is then used to simulate varying aperture from f/2 to /01 producing different degrees of defocus blur. (iii) Shutter Speed: We simulate exposure durations within the range (0, 1] seconds by adjusting radiance in the HDR domain and converting it RGB through a differentiable ISP pipeline 9 31 36.
4 Method
Our CamEdit framework adopts the instruction-driven editing paradigm of IP2P (5), while building upon the diffusion backbone illustrated in Figure 3 Given camera-parameter instruction, first apply continuous parameter prompting as described in Section 4.1 which enables fine-grained prompt conditioning. The resulting embeddings, combined with the input image, are then fed into the transformer equipped with parameter-awand modulation modules described in Section 4.2 which inject parameter-spedee feature modulation into the generation process.
4.1 Continuous Parameter Prompting
Directly learning parameter embeddings and appending them to other text features, while bypassing the text encoder, introduces a distributional mismatch with the frozen text embedding space. This misalignment degrades generation quality and hampers convergence, shown in Section [533] To address this, our continuous parameter prompting synthesizes parameter representations by interpo- lating between anchor embeddings of adjacent discrete tokens within the text embedding space. The
PhotoGen synthesizes samples by continuously sampling camera parameters over 3K fixed images, reducing scene diversity and affecting realism.

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Input
Encodor
VAE Decoder
Output
Transformer Blocks
Diffusion Transformer
Iterative Denaising
Discrete "0.4"
Continuous Parameter Prompting
Promees Embediiingg
Param Aorn-Awwre
Linear
Attention
Modulatinn
Continuous P 0.53
"O.5"
Tokenizer
Text Encode
0.63
70.6"
Tokoo Ml
"Adjust the image with aperture f/4Ps,"
Block
KN
<params
Figure 3: Framework Overview: The continuous parameter prompting module obtains a continuous discrete camera token embeddings. This
parameter embedding via learnable interpolation embedding replaces the placeholder token in the text embedding space, while preserving the original prompt structure. The parameter also injected into the diffusion transformer via the parameter- aware modulation module, which adjusts features to reflect the corresponding visual effects.
resulting embedding replaces the parameter placeholder in the encoded prompt, ensuring perceptual continuity across parameter variations. This mechanism requires no modification to the text encoder and integrates seamlessly into Liffusion-based frameworks.
Let E R denote a continuous camera parameter, and anchor values with associated learnable embeddings (ei,...,ex, ex] } Rd, where each e obtained by encoding the anchor token via the frozen CLIP tokenizer and text encoder. For any E [pt, P++1) the parameter embedding is computed as:
PK be a set of predefined discrete
ep Linear([e;, e1+1)) MLP())p)).
where o(p) [0, 1] represents the normalized relative position of y between anchors p; and p:+1- The linear projection aggregates the semantic content of the two neighboring embeddings, while MLP, implemented as two-layer feed-forward network with ReLU activation, introduces pesition-dependentt residual to capture fine-graines variation. The final embedding e, replaces the parameter placeholder in the encoded text prompt representation.
4.2 Parameter-Awat Modulation
Camera parameter variations primarily influence the spa- tial appearance of an image while preserving its underlying semantic content. Such changes include spatial transfor- mations, for example, depth-dependent focus shifts across foreground and background regions [59]. Additionally, parameters such as shutter speed induce global exposure changes 30], motivating channel-wise feature modulation.
F
F
and sample
+
MLP
MLP.
Anghool
To effectively model visual effects, we modulate inter- mediate features conditioned on the parameter through two complementary operations as shown in Figure To improve parameter sensitivity and information flow, the modulation applied after the self-attention layers in each transformer block, where features contain rich contextual dependencies. The first component, geometry- -aware aware spatial modulation, models lens-ind induced geometric distortion and depth-dependeet focus transitions. It predicts spatial displacement field that perturbs feature coordinates based on visual features and the input parameter. Specifically. we apply 2 x 2 average pooling to the input feature map F and feed the pooled representation, together with p, into parameter-adaptive MI.P
p
Param-Aware Modulation
Figure 4: Illustration of parameter-
modulation.
AG MLP, (AvgPool (F), 2x xxW
(2)
5

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where AvgPool(.) denotes average pooling over non-overlapping 2 x 2 patches. The warped feature then computed via:
Fg perid_sample[[ (F, Ghuse + AG).
where Glase represents the canonical coordinate grid of F as defined in 25 and grid_ sample performs differentiable sampling of F at continuous coordinates.
The second component, channel-wise modulation, adjusts feature amplitudes to capture global appearance variations. We compute channel-wise scaling and bias terms as follows:
Fp - (p) Fg + Bpp), where (p),, = (Avg(ovg
E RR.
(4)
Both MLPg g and MLPp are implemented as single-layer feed-forward networks with intermediate ReLU activation. Then Fp is forwarded the subsequent transformer block.
5
Experiments
5.1 Implementation Details
Training and Inference. We adopt Stable Diffusion (SD3) 10]
our backbone due its strong generation quality and color fidelity. The majority of SD3 weights are kept frozen to preserve its pre-trained capacity. We update only the text embedding layer to learn anchor tokens and enable our learnable parameter interpolation. For each task, we predefine 10 anchor tokens via the tokenizer to guide training. The transformer is initialized from 772. and we fine-tune lightweight adapters using LoRA A [22], combined with our physics-driven adaptation module. We train the model using Adam W [88] with learning rates of 1e-5. Training is conducted on 512 x 512 resolution images with batch size of 32 for 50 epochs. During inference, the model requires only an input image and an instruction specifying any continuous camera parameter value within the valid range.
Metries. We evaluate performance from three perspectives: perceptual quality, content preservation, and parameter control accuracy. Perceptual quality is measured using NIQE [i] and MUSIQ [28). which assess naturalness and visual fidelity without references. Content preservation is quantified via DINO similarity (6), capturing semantic alignment between the source and edited images. To assess parameter control, we compute the L1 eтror between the instruction instruction-specifeed target and the estimated parameter extracted from the generated image. Estimation follows the physically grounded procedure described in Section 3
We evaluate 200 images across varying parameter settings.
5.2 Comparison to State-of-the-AAt Methods
Comparison Methods. We firstly compare our method against state-of-the-art diffusion-based baselines, including editing models such as SuperEdit [41), UltraEdit (72), and In-Context Edit [47),
well as the camera-aware 12V model PhotoGen [70]. To evaluate PhotoGen (70), generate images using prompts sampled from GPT-4o to simulate realistic text-based generation requests. We retrain UltraEdit on our CamEdit50K enable camera-awarr editing, denoted UltraEdit*.
Beyond diffusion- based baselines, we further compare our method with other approaches across all editing tasks. For aperture editing, we compare with BokehMe [45). BRVIT 43 and DrBokeh (57).
For focal plane editing, we evaluate against BokehMe [45), MPIB [46,, and DrBokeh [57]. For shutter speed editing,
include advanced low-light and exposure aware methods such SCI [39],
CycleR2R (36), and CLODE (26). All methods are tested under a consistent exposure configuration and evaluated for their ability adapt image brightness while preserving both structural integrity and perceptual quality.
Quantitative Comparison. As shown in Table 2 (a), method consistently outperforms other editing across all tasks in NIQE, DINO, and control error. Compared to the retrained Udt model, our method achieves an over 40% relative reduction in average control error, demonstrating substantially improved parameter alignment. Existing editing models do not explicitly incorporate camera parameters, limiting their ability generate parameter-consister results. Although UltraEdit* benefits from CamEdit5OK supervision, remains less precise than our approach.
We further evaluate performance via GPT-4o across photographic realism, content preservation, and parameter accuracy. We further assess performance using GPT-4o evaluation and a user study with 15 photography experts. Each participant evaluated 20 image sets per task across the three tasks, scoring

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Aperture
Speed
UlreFdi
SuperFdr
Ultaaddt
Curs
CDT-4o Evaluntio Score
GPT-4o Evaluntion Score
GFT-4 Eva untion Score
Photographic
Aperture
Focal Plane
Shutter Speed
Ultraddit
SuperEdit
Content
UtrsEdit
Preserosion
Ours
16
UserStudl
Figure 5: GPT-4o evaluation and user study across three dimensions: photographic realism, content preservation, and camera parameter accuracy, using 0-10 scale (higher better). Table Quantitative comparison across camera-aware editing methods under aperture, focal-plane, and shutter-speed control. Best results bold.
(a) Diffusion-Base Methods
Aperture
Focal Plane
Shutter Speed
Method
NIQEJ
DINOt
Error]
NIQEL
DINO1
Error]
NIQEJ
DINO1
Error
SuperFdit 41
4,43
0.73
5
5.28
0.73
-05
4.86
0.75
~0.5
UltraEdit
72
4.58
0.70
5
5.57
0.74
~05
5.02
0.72
~0.5
In-Context Edit
4.30
0.78
5
4.91
0.80
~05
4.29
0.81
~0.5
PhotoGen
70
6.65
1.31
6.21
0.28
6.11
0.19
Ultradit
4.21
0.78
1.87
5.14
0.79
0.25
4.69
0.88
0.23
Ours
3.34
0.83
0.60
4.46
0.82
0.15
4.28
0.93
0.11
(b) Other Methods
Aperture
Focal Plane
Method
Method
NIQEJ MUSIQU DINO1 Error
NIQEJ MUSIQ1
DINO1 Error]
BokchMe 45
4.62
59.70
0.82
0.62
BokehMe 45
5.19
48.97
0.79
0.22
BRVIT 45
6.53
50.72
0.71
MPIB 46
5.14
49.05
0.78
0.18
DrBokeh [57
3,81
62.26
0.81
0.58
DrBokeh 57
5.07
47.59
0.80
0.17
Ours
3.34
62.91
0.83
0.60
Ours
4.46
52.64
0.82
0.15
photographic realism, content preservation, and parameter accuracy. As shown in Figure 5 method achieves the highest average scores on all dimensions. Remeensonss.Relaivee to UltraEdit", our CamEdit improves realism by 8% and parameter-control accuracy by 10%, demonstrating consistently higher realism, stronger content preservation, and more precise control.
As shown in Table 2 (b). our method consistently outperforms rendering- based basclines most metrics, such as DrBokeh [57]. BokehMe [45] on both aperture and focal plane editing tasks. For aperture editing, approach yields 23% lower control error compared to baselines. Relative to the strongest competitor, DrBokeh, our method improves NIQE by 12% and reduces error by 24%. These results highlight the benefit of continuous parameter control in diffusion models, enabling physically grounded and perceptually faithful image editing.
Qualitative Comparison. As shown in Figure 6 our method achieves fine-grained and continuous control across all camera parameters, demonstrating clear parameter awareness. Existing diffusion- based editing models lack such capability due to the absence of camera supervision during training. UltraEdit retrained on our dataset, shows improvement, but its discrete prompting leads to occasional mismatches when interpolating unseen values. In contrast, better fidelity, particularly around depth-sensitive regions such as foreground boundaries, owing to our parameter-aware modulation. We also compare with the generative model PhotoGen (70), where our results exhibit higher photographic realism and more coherent spatial structure. This improvement stems from our seiting-bbsed formulation and the of real image pairs during training. Our method handles diverse scenarios with realistic parameter effects, such as light flares from aperture adjustment in nighttime scenes exposure refinement that enhances visual aesthetics.
method ensures smooth transitions and
Figure further supports our findings. Under aperture variation, our method preserves fine details like hair strands and object contours. For focal plane editing, maintain sharpness in in-focus areas,
7

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operture fepp
p=3
P=5.6
Input
UltraEdit
Ours
SuperEdit
Ours
In-Context Edit
Ours
with focal plane spa
p 0.315
p 0.57
p 0,7
- 0.115
p 0.51
p= 0.7
Input
UltraEdit*
Ours
...wtth shutter speed sp>"
p 0.25
p=
p 0=7.1
p 0.25
p=
p 0.71
Input
Ours
PhotoGen
Additional Results (Ours)
"Adjust image with shutter speed 0.54."
"Adjust image with aperture 1/8."
"Adjust image with focal plane 0.15 0.9."
"Adjust image with shutter 0.72."
image with aperture f/3.5."
"Adjustt with facal plane 0.24 0.6."
Figure 6: Visual comparison with diffusion-based methods. Our results illustrate smooth and perceptually consistent edits under various instructions.
Aperture
0.13
0,5
DrBokeh
Ours
f/2
Shutter
Inference
CLODE
Ours
BRVIT
BokehMe
Ours
Figure 7: Visual comparison with other methods. Our method achieves finer detail and more realistic photographic effects compared traditional rendering pipelines.
especially on architectural edges. In shutter speed control, our outputs adjust motion-related brightness while preserving photographic style. These results confirm that CamEdit delivers physically consistent edits with precise control and high visual fidelity.
5.3
Ablation Study
In this section, we analyze the components of CamEdit and the composition of CamEdit50K, All ablations conducted on the focal-plane task.

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Taininn
Pompling
Co ROL Plire. Promping
000
004
Prosery
Toar Stons
(a)
Rasamatte Latt
(b)
RAM
[c]
w/ PAM
Figure 8: (a) Training loss comparison between direct parameter embedding and ConPrompt. (b) T-SNE visualization showing more semantically aligned embeddings with ConPrompt. (c) Visual comparison with and without PAM.
Table 3: Ablation study on key components of Table 4:
Performance with different synthetic-to-real
our CamEdit.
ratios of training dataset.
Method
NIQE, NIQE,MUSSQQ MUSIQ; Error
Syn:Real
NIQEL
MUSIQ
DINO
Error.
w/o ConPrompt
4.31
54.26
0.80
0.25
0:1
4.81
53.73
0.75
0,23
w/ Direct Embed.
5.25
52.41
0.81
0.27
1:1
4.76
53.92
0.80
0.21
w/o PAM
4.79
54.10
0.82
0.18
1:0
4.93
53.14
0.76
0.15
Ours (Full)
4.46
52.64
0.83
0.15
CamEdit5OK
4.46
52.64
0.83
0.15
Continuous Parameter Prompting. As shown in Table 3] removing continuous parameter prompt- ing (w/o ConPrompt), leads to degraded control accuracy and lower perceptual quality. Similar inconsistencies are observed in the retrained UltraEdit*, where editing results lack smooth transitions and do not align well with the target parameters, as illustrated in Figure [
We further evaluate a direct parameter embedding learning variant (w/ Direct Embed.), which bypasses the text encoder and learns parameter embeddings independently. It results in lower visual quality, and unstable training, as shown by higher loss and slower convergence in Figure [ (a) and (b). In contrast, ConPrompt interpolates between pre-defined anchor tokens within the frozen text space, yielding smooth, semantically meaningful embeddings that improve control, fidelity, and stability.
Parameter-Aware Modulation. Parameter-aware modulation improves both parameter accuracy and image quality, as evidenced by the performance drop in the "w/o PAM" variant in Table 3 This is because different camera parameters induce global shifts in scene appearance, and PAM enables the model adapt feature representations accordingly. As shown in Figure (c), removing PAM results in unnatural transitions in defocus regions, particularly around human silhouettes, where the blur at object boundaries becomes abrupt. We also evaluate the injection of continuous camera-parameter features into the diffusion timestep embeddings in place of PAM. Compared with CamEdit, NIQE higher by 0.30, DINO is lower by 0.03. and Error higher by 0.09, indicating weaker parameter control due to the absence of localized spatial modeling.
Composition of CamEdit5OK. We analyze CamEdit50K by varying the ratio of synthetic real data, as shown in Table Training on real data alone lacks scene diversity and parameter coverage, leading to weaker perceptual quality and control. Adding synthetic data a 1:1 ratio improves over real-only training, though gains are limited by the smaller total size. Synthetic-only training scales well and enhances control, but visual fidelity lags without real-image guidance. Our CamEdit50K, combining available synthetic and real data, delivers the best parameter control and visual quality, driven by the scale of synthetic data and the fidelity of real data.
6 Conclusion
We present CamEdit, framework for photorealistic image editing with continuous control over camera parameters such as aperture, focal plane, and shutter speed. It features a parameter-aware design and supported by CamEdit50K, a hybrid dataset with paired images and varying camera settings. CamEdit enables visually consistent, photorealistic edits and lowers the barrier for users to manipulate camera parameters through images, with potential applications in education, simulation, and creative industries. While effective on key controls, it does not yet support all camera parameters and cannot recover focus from blurred regions, which remains fundamentally challenging and presents valuable direction for future research.

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6.1 Acknowledgments
This work was supported in part by the Shenzhen Science and Technology Program (No. KOTD20211010335901 the National Natural Science Foundation of China (No. 62025604), and the -IE-SShatchipp Research Fund (No. 2024-08). We gratefully acknowledge the creators and maintainers of the public datasets and open-source models used in this work.
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Reviews:
[{"review_id": "VbRecwM2qQ", "reviewer": "Reviewer_rxHt", "summary": "CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing presents a diffusion generative model to modify images based on camera parameters using text prompts. The task is controlled photorealistic conditioned generation to simulate the change of aperture, focal plane and shutter speed parameters using continuous physical quantities. To achieve this a continuous parameter embedding learns to interpolate over discrete camera tokens that are merged with the original prompt tokens. Additionally, features are modulated after the self-attention layers of the DIT conditioned on the camera parameters from the input prompt.\nFor training of the newly added modules and fine-tuning of the overall model using LoRA, a new dataset is generated using images from various photographic datasets and synthetic data. The photographic data is annotated using a custom annotation pipeline for camera parameters. In the quantiative evaluation the method reaches state-of-the-art in several reference-free metrics and an evaluation using GPT-4o.", "questions": "- How does the model output compare to ground truth footage shot with a digital camera and photographic lens, for example? Do the parameter scales make sense in this case and how are additional lens artifacts like aberrations, distortion etc. handled by this approach? Does the diffusion model know about those? Or are they getting lost due to the limitations in the data generation process? Some comparison would be critical to justify the claim of photorealism here, I think. This could also be achieved with simulated data.\n\n- What camera model is the basis of the problem statement? It might be better to introduce a common camera model that explains all the mentioned effects early on in the method section (move some parts from the supplements?)\n\n- What are the advantages of textual input? Why not using a different e.g. slider based input which might be more intuitive for users of photo editing software?\n\n- An important part of the work is the data generation that builds a nice pipeline for image annotation of camera related parameters. It would be good to include the image sources in the main paper here (instead of the supplementary) I think as this lead to some questions that weren't answered by a reference, for example.\n\n- How consistent are the generations? An evaluation over multiple generations might be interesting here. At least reporting the standard deviations in the tables could give some hint.", "limitations": "Model limitations are adequately discussed, the societal impact of manipulations of photographic material should probably be added.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strength:\n- The paper convincingly introduces a method to improve the controllability in generative image manipulation tasks that are related to changing camera parameters. This is a relevant task for photography and design applications and might also help simulation tasks for autonomuous and robotic systems. \n- The dataset generation pipeline shows some effort and the dataset could be useful for the research community.\n- The method achieves good scores in all applied metrics. \n\n\nWeaknesses:\n- In the context of the related works and the methods problem setting, works like ConceptSliders [1] would be relevant, too. \n- While relative adjustments look plausible in the qualitative analysis and work well in no-reference metrics, some analysis against actually shot ground truth is missing, which would be available from any professional digital photo camera.\n- In the image editing ecosystem text-based interaction might not be the most efficient way to change image parameters. \n- The limitation of only editing one parameter at a time is also limiting the applicability. \n- The shutter parameter seems to work like a gain / exposure adjustment here. While this is one of the effects of changing the shutter time there is also a change in how scene motion is rendered. This is missing from the analysis and the shown results. E.g. if I increase the shutter speed I would expect to see more motion blur in the scene (from moving objects and potentially camera shake). This weakness also seems to be reflected in the lower photorealism score in the GPT-5o evaluation.  Additional image changes like noise patterns, aberrations and distortions are not discussed in the paper, although they have quite some impact on the perceived photorealism of an edit.\n- While ChatGPT-4o might be a good proxy, a user study with real human subjects would be interesting and is missing in the evaluation.\n- Camera parameter modification does not work in all directions equally well (as some destructive effects cannot be undone by the model). This might be out of scope for a single work but would be an interesting trajecory for further research.\n\nSmall typos: \n- Figure 5 caption: \"Additional\" -> \"Additionally\"\n- BrokehMe instead of BokehMe, p.6, l.216\n\n1: Gandikota et al. Concept Sliders: LoRA Adaptors for Precise Control in Diffusion Models. ECCV 2024.", "was_revised": true, "final_justification": "Thank you for the detailed answers. Overall, many questions have been addressed adequantely and I'm happy to increse the final rating in line with the other reviewers.\n\nWith the added evaluations, including the user study, and the refinements of the text as proposed in the rebuttal I can see the paper as part of the conference program. The method outperforms prior work and the authors manage to show that also a multi-parameter model variant can be trained which would enable broader application.", "rebuttal": "We sincerely thank you for your review. Below are your concerns and our corresponding responses:\n>W1: Discussion of related work such as ConceptSliders.\n\nConceptSliders is designed for common editing tasks such as changing human age and expressions or performing style transfer. However, it is not directly applicable to photorealistic image editing. However, our work proposes a photorealistic editing framework that supports tasks such as focus swapping, aperture adjustment, and exposure modification. We will include this discussion in the related work section.\n\n> W2 & Q1: Evaluation against ground-truth captures from professional digital cameras.\n\nWe evaluate CamEdit on three publicly available datasets with ground-truth captures from digital cameras: VABD [1] for aperture, EBB Val [2] for focal plane, and SICE [3] for shutter speed.  As shown in Table R1‑1, CamEdit outperforms both fine-tuned image editing methods (UltraEdit*, retrained on CamEdit50K) and rendering-based approaches (DrBokeh, CLODE) across all tasks, achieving superior SSIM and LPIPS scores.\n\nTable R1-1: Quantitative comparison of CamEdit on three real datasets (VABD for Aperture, EBB for Focal Plane, and SICE for Shutter Speed). \n| Task (Dataset)           | Aperture (VABD)             | Focal Plane (EBB Val)           | Task (Dataset)                   | Shutter Speed (SICE)            |\n|---------------------------|------------------------------|----------------------------------|-----------------------------------|----------------------------------|\n| Method                    | SSIM ↑ / LPIPS ↓            | SSIM ↑ / LPIPS ↓                | Method                            | SSIM ↑ / LPIPS ↓                |\n| DrBokeh                   | 0.79 / 0.27                 | 0.79 / 0.24                     | CLODE                             | 0.84 / **0.17** |\n| UltraEdit*                | 0.71 / 0.34                 | 0.73 / 0.31                     | UltraEdit*                        | 0.74 / 0.25                     |\n| **CamEdit (Ours)** | **0.82** / **0.26** | **0.80** / **0.22** | **CamEdit (Ours)** | **0.86** / 0.18                     |\n\n[1] Andrey Ignatov et al. AIM 2020 Challenge on Rendering Realistic Bokeh. ECCVW, 2020.  \n[2] Kang Chen et al. Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance. arXiv, 2024. \n[3] Jianrui Cai et al. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images. TIP, 2018.\n\n> W3&Q3: Advantages of text-based input vs. other controls.\n\n- Embedding camera parameters as textual inputs leverages the T2I diffusion model’s prior knowledge, improving the model’s understanding of the parameters and performing more accurate camera parameter editing, as demonstrated in Section 5.3 and Figure 7 of the main paper, compared to direct parameter mapping or linear approaches.\n   \n- Moreover, the digital parameters of textual inputs can be directly mapped to slider‑based or other controls, with the editing software’s UI adapting to the user’s choice.\n\n> W4: Concern about editing only one parameter at a time.\n\nWe conduct an experiment where the model is trained to adjust all three parameters simultaneously. In this experiment, we double the number of training parameters and include 5,000 synthetic images with multi-parameter variations. As shown in Table R1‑2, the unified model demonstrates accuracy and output quality comparable to single-parameter control, confirming its ability to effectively manage multi-parameter adjustments. We will incorporate these updated results and provide further clarification on this capability in the revised manuscript.\n\nTable R1-2: Performance of CamEdit with single-parameter vs. joint multi-parameter control, evaluated across 200 images with various parameter settings.\n\n| Task             |           Aperture            |          |          |           Focal Plane           |          |          |           Shutter Speed           |          |          |\n|--------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|                    | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| **CamEdit (Single-Parameter)** | 3.34         | 0.83   | 0.60   | 4.46             | 0.84   | 0.15   | 4.28               | 0.93   | 0.11   |\n| **CamEdit (Joint-Parameter)** | 3.42         | 0.84   | 0.65   | 4.70             | 0.83   | 0.17   | 4.12               | 0.91   | 0.15   |  \n\n> W6: The user study with real human subjects.\n\nWe conducted a user study with 15 participants who have expertise in photography. Each participant evaluated 20 image sets per task across all three tasks, rating photographic realism, parameter accuracy, and content preservation on a 0–4 scale. \nAs shown in Table R1‑3, CamEdit consistently achieves the highest scores across all metrics, confirming the validity of our results under human evaluation.\n\n\nTable R1-3: User Study Scores (0–4 scale) on Photographic Realism, Parameter Accuracy, and Content Preservation\n\n| Task           | Metric                  | UltraEdit / SuperEdit / UltraEdit* / CamEdit  |\n|----------------|-------------------------|-----------------------------------------------|\n| Aperture       | Realism ↑                 | 2.26 / 2.70 / 3.42 / **3.88** |\n|                | Accuracy ↑               | 1.36 / 1.02 / 3.18 / **3.76** |\n|                | Preservation ↑           | 2.10 / 2.76 / 3.38 / **3.87** |\n| Focal Plane    | Realism ↑                | 2.71 / 3.11 / 3.31 / **3.75** |\n|                | Accuracy ↑               | 1.30 / 1.58 / 2.46 / **3.71** |\n|                | Preservation ↑           | 1.80 / 2.83 / 3.14 / **3.81** |\n| Shutter Speed  | Realism ↑                | 2.22 / 3.39 / 3.58 / **3.84** |\n|                | Accuracy ↑               | 2.76 / 1.58 / 2.28 / **3.80** |\n|                | Preservation ↑           | 2.00 / 3.26 / 3.52 / **3.94** |\n\n> W5&Q1: Concern about the lack of motion blur modeling for shutter speed and omission of lens artifacts (e.g., distortions). \n \n1. **On motion blur and shutter speed:**  \n- CamEdit models shutter speed via exposure adjustment, following standard single-image shutter modeling practices (e.g., PhotoGen [4]), which are widely used in computational photography.\n- Motion-induced effects like blur from object or camera movement require multi-frame data and are beyond the scope of this single-image editing framework.\n- The slightly lower GPT‑4o score is mainly due to brightness changes, as the model tends to penalize low-light images. Nonetheless, as shown in Table R3‑2 and R3‑3, CamEdit consistently outperforms existing methods in both human studies and comparisons with real ground-truth data.\n\n2. **On lens artifacts, parameter scales, and real-camera comparisons:** Complex lens artifacts such as chromatic aberrations and geometric distortions are not controlled by camera parameters, and are therefore outside the scope of this work.\n\n[4] Yu Yuan et al. Generative photography: Scene-consistent camera control for realistic text-to-image synthesis. CVPR, 2025.\n\n> W7: Unequal effectiveness of camera parameter adjustments, as some destructive effects cannot be undone.\n\nAs noted in Lines 34–36 and 89–94, CamEdit is consistent with existing rendering-based methods and physical camera models, and is designed for precise camera parameter adjustments. When input image details are lost, reversibility is not feasible for any current editing framework, as the missing information cannot be recovered. Addressing such cases would require dedicated image restoration strategies, which we identify as a promising direction for future research.\n\n> Q2&Q4: Clarifying the underlying camera model and data pipeline.\n\nIn the revised version, we will integrate the camera model into the main method section for a unified explanation of all referenced effects, and move the data generation process and image sources from the supplementary into the main text for clarity.\n\n> Q5: Consistency of generations\n\nWe evaluate the consistency of CamEdit across different parameter settings using 200 images per task. For each image, we compute the mean and standard deviation of the DINO similarity between the generated outputs and their corresponding inputs, across five different parameter settings for the same image.\n\nCamEdit demonstrates high consistency with low variation:\n\n**Aperture:** 0.83 ± 0.09  \n\n**Focal Plane:** 0.84 ± 0.11 \n\n**Shutter Speed:** 0.93 ± 0.06  \n\nFor aperture and focal plane, we further analyzed foreground and in-focus regions separately, where the DINO standard deviations drop to 0.04 and 0.05, respectively. These results indicate that CamEdit’s edits are stable, with negligible changes outside the parameter-driven effects.\n\nQualitative results (Figure 1, Figure 5 in the main paper; Figure S3, Figure S5 in the supplementary) also show that CamEdit modifies only the target camera attributes without affecting unrelated scene content, further confirming the consistency of CamEdit.\n\n> L: The societal impact of photographic manipulations.\n\nIn the revised version, we will explicitly discuss potential societal impacts, including the risk of misuse for deceptive image manipulation, and outline mitigation strategies such as watermarking and edit-detection mechanisms.\n\n> About Typos.\n\nWe will correct all typos in the revision."}, {"review_id": "xqRy3Nd8B6", "reviewer": "Reviewer_ju7m", "summary": "The paper proposes a diffusion-based framework, CamEdit, for photorealistic image editing that enables continuous control of camera parameters, i.e., aperture, focal plane, and shutter speed. The model is adapted from a pre-trained Stable Diffusion 3 and contains two strategies: (1) a continuous parameter prompting mechanism that interpolates embeddings for smooth parameter transitions, and (2) a parameter-aware modulation (PAM) module that adapts spatial and channel-wise features to reflect physical camera effects. To support training and evaluation, they construct CamEdit50K, a new dataset combining real and synthetic image pairs with annotated camera settings. Experiments demonstrate that CamEdit achieves state-of-the-art performance in visually consistent, parameter-controlled edits, surpassing both diffusion-based baselines and classical rendering methods.", "questions": "Please see the weaknesses section.", "limitations": "The checklist claims that the limitations and potential negative social impact are discussed in Section 6, but that paragraph is too vague and does not provide thorough descriptions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "**Strengths**\n\n- The paper addresses an interesting and practical problem of photorealistic image editing with continuous camera parameter control, which is relatively underexplored.\n\n- A new dataset, CamEdit50K, is introduced, providing paired real and synthetic images annotated with continuous camera settings, which is valuable for training and benchmarking camera-aware editing models.\n\n- The proposed method presents a reasonable and efficient approach to injecting controllability over continuous camera parameters while largely preserving the pre-trained diffusion model’s weights.\n\n\n**Weaknesses**\n\n- Although the dataset is claimed as one of the main contributions, the paper lacks a thorough analysis of the dataset’s factors, such as how incorporating synthetic data impacts learning, or how the accuracy of estimated camera parameters from real images affects the editing performance of the trained model. Furthermore, it remains unclear whether the design choice of varying only one parameter at a time in synthetic data is deliberate, and whether including examples with simultaneous variations in multiple parameters might help disentangle control and improve learning.\n\n- The clarity of Section 4, particularly Section 4.2, is insufficient. The two components of the parameter-aware modulation module are not visually depicted in the main method figure, and the terminology is somewhat confusing. For instance, it is unclear why average pooling operations are labeled as “Down” instead of using a standard term like “AvgPool.” While grid_sample is a common function in PyTorch, the authors should explicitly describe its operation and role to ensure clarity for readers unfamiliar with its mechanics.\n\n- The paper does not provide a reasonable baseline comparison for the design of the Parameter-Aware Modulation module. A straightforward alternative, such as directly injecting continuous camera parameter features into the timestep embeddings with zero initialization, should be compared to demonstrate the advantages of the proposed approach.\n\n----\n\n**Conclusion**\n\nThe paper tackles an interesting and practical problem and achieves reasonable performance; however, it lacks analysis of the dataset, which is presented as a core contribution, lacks reasonable baselines for the continuous camera parameter injection, and requires significant improvements in the clarity of Section 4.", "was_revised": true, "final_justification": "The rebuttal presents additional results to analyze the dataset curation and the design of the PAM module, providing better support for the claims made in the paper. I increased the score from 3 to 4.", "rebuttal": "We sincerely thank you for your review. Below are your concerns and our corresponding responses:\n\n> W1: Analysis of dataset composition and the accuracy of estimated real-image parameters.\n\nWe conduct two sets of ablation studies to address these concerns.\n\n**1. Impact of CamEdit50K composition (Table R2-1).**  \nWe train models on only real data, only synthetic data, and the full CamEdit50K dataset.\n- Training only on real data (~7k) results in lower perceptual quality and parameter accuracy due to the limited diversity of real scenes and insufficient parameter coverage.\n\n- Training only on synthetic data (~40k) achieves more consistent parameter estimation due to its large quantity. However, its visual quality is limited due to the lack of guidance from real captured images.\n\n- Full CamEdit50K, by combining both available synthetic and real data, achieves better parameter control and visual quality. This improvement is driven by both the quantity of synthetic data and the quality of the real data. As shown in Figure 6 of the main paper, when compared with the rendering-based method (BokehMe) used for our synthetic data, our CamEdit significantly improves visual results, reflecting the effectiveness of combining real and synthetic data.\n\nTable R2-1: Performance of CamEdit with different dataset composition.\n\n| |           Aperture            |          |          |           Focal Plane           |          |          |           Shutter Speed           |          |             |\n|-------------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|  Datset  | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| Only on synthetic data       | 3.91|\t0.82|\t0.66\t    | 4.93            | 0.76  | 0.15    | 4.19               | 0.87  | 0.12    |\n| Only on real data     | 4.05         | 0.76  | 1.76    | 4.81            | 0.75  | 0.23    | 4.21               | 0.85  | 0.14    |\n| **CamEdit50K**      | **3.17**         | **0.84**  | **0.57**    | **4.51**            | **0.83**  | **0.13**    | **4.12**               | **0.91**  | **0.09**    |\n\n**2. Effect of estimated camera parameters (Table R2-2).**  \nWe compare models trained with synthetic data combined with either only EXIF-annotated real data, only estimated-parameter real data, or the full dataset.\n\n- Training on estimated-parameter data improves perceptual quality relative to EXIF-only data, as the estimated subset spans more diverse scenes. Real data with estimated parameters also contribute to improvements in both the visual quality and parameter control.\n- The full dataset achieves the best overall performance, confirming the accuracy of our estimation method. Estimated parameters allow the inclusion of more real paired data into the dataset, further enhancing the model’s performance.\n\nThese results demonstrate that both real and synthetic data are essential for optimal performance, and our estimated camera parameters improve coverage and help the model generalize, ensuring reliable editing quality.\n\nTable R2-2: Impact of estimated vs. EXIF camera parameters on editing performance in aperture task.\n| Datset     | NIQE ↓ | DINO ↑ | Error ↓ |\n|--------------------|--------|--------|--------------------|\n|  Synthetic data + Only EXIF          | 3.43   | 0.82   | 0.60               |\n|  Synthetic data + Only Estimated     | 3.31   | 0.83   | 0.61               |\n| **CamEdit50K**      | **3.17**   | **0.84**   | **0.57**               | \n\n>  W1: Design choice of varying one parameter at a time vs. multiple parameters.\n\nWe conduct an experiment where the model is trained to adjust all three parameters simultaneously. In this experiment, we double the number of training parameters and include 5,000 synthetic images with multi-parameter variations. As shown in Table R2‑3, the unified model demonstrates accuracy and output quality comparable to single-parameter control, confirming its ability to effectively manage multi-parameter adjustments. We will incorporate these updated results and provide further clarification on this capability in the revised manuscript.\n\nTable R2-3: Performance of CamEdit with single-parameter vs. joint multi-parameter control, evaluated across 200 images with various parameter settings.\n\n| Task             |           **Aperture**            |          |          |           **Focal Plane**           |          |          |           **Shutter Speed**           |          |          |\n|--------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|                    | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| **CamEdit (Single-Parameter)** | 3.34         | 0.83   | 0.60   | 4.46             | 0.84   | 0.15   | 4.28               | 0.93   | 0.11   |\n| **CamEdit (Joint-Parameter)** | 3.42         | 0.84   | 0.65   | 4.70             | 0.83   | 0.17   | 4.12               | 0.91   | 0.15   |  \n\n> W2: Clarity of Section 4.2 and the description of the Parameter-Aware Modulation module.\n\nIn the revised version, we will:\n\n1. Improve the method figure by explicitly depicting the two components of the parameter-aware modulation module and their roles within the diffusion backbone.  \n\n2. Clarify terminology by replacing the shorthand “Down” with the standard term “AvgPool (2×2)” to avoid confusion.  \n\n3. Explain grid_sample with a concise description of its operation and purpose. In our framework, it is used to warp features according to the camera parameter offsets, enabling spatially aligned modulation of the diffusion features.\n\nWe will enhance the readability of Section 4, ensuring that the parameter-aware modulation module is easy to understand.\n\n> W3: Baseline comparison for the Parameter-Aware Modulation (PAM) module. \n\nWe conduct more ablation studies to demonstrate the effectiveness of our PAM. \n\n- Replace by TimeStep: injecting continuous camera parameter features into the diffusion timestep embeddings with zero initialization.  \n- Replace by Conv: injecting parameters by replacing PAM with convolution-based layers that have the same number of parameters as PAM, but without spatial warping or channel-wise scaling.\n\nAs shown in Table R2‑4, both baselines underperform compared to PAM. TimeStep injection yields weaker parameter control due to the absence of localized spatial modeling. Conv injection provides partial improvements in parameter consistency but cannot explicitly capture geometric or global appearance variations.\n\nBy contrast, PAM, which combines spatial warping and channel‑wise scaling, achieves the best trade‑off between perceptual quality and parameter accuracy. These results confirm that our PAM design is critical for robust and precise parameter‑aware editing.\n\n Table R2-4: Comparison of PAM vs. simpler baselines in aperture task.\n\n| Method                | NIQE ↓ | DINO ↑ | Error ↓ |\n|-----------------------|--------|--------|--------------------|\n| Replace by TimeStep   | 3.47   | 0.81   | 0.66               |\n| Replace by Conv       | 3.41   | 0.83   | 0.63               |\n| **Full (PAM)**        | **3.17**   | **0.84**   | **0.57**               |\n\n> L: Discussion of limitations and potential social impact in Section 6.\n\nWe will expand Section 6 in the revised version to provide a clearer discussion. Our work already covers a diverse set of camera parameters, and in future work we plan to extend the approach to more parameters and editing tasks. Moreover, we will also discuss the societal risks, especially wrongful image manipulation, and suggest controls like watermarking and post‑processing detection methods."}, {"review_id": "MC78bpOt4L", "reviewer": "Reviewer_DyLt", "summary": "The authors aim to address fine-grained photorealistic image editing problems. To this end, they propose a diffusion-based editing framework consists of continuous parameter prompting and parameter-aware modulation module to achieve this task. They also propose a  training dataset with 50k samples to facilitate model training. Extensive experiments shows the effectiveness of the method and each modules.", "questions": "I d like the authors to address the concerns about the dataset settings and evaluation methods part in the ''weakness'' part.", "limitations": "The authors should discuss limitations about the work.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "strengths:\n1. The target problem is interesting and significant to both image editing area and real applications. Existing manipulations cannot achieve fine-grained photorealistic image editing.\n2. The paper is well written and organized. The main method is easy but effective, with the solid ablation studies. Also the proposed dataset will be a great contribution.\n\nweakness:\n1. some typos should be checked. (e.p line 184 G_base.)\n2. The authors introduce the motivation of continuous parameter prompting and parameter-aware modulation in Sec 4.1 and Sec. 4.2. However, I d like the author to expose more ideas about how to link the proposed methods into the problem. \n3. The proposed dataset is a contribution. Thus, the authors need to give more discussions on it. For example, the authors take the two ways to get the data. So, whats the pros and cons of each method? Will different data ratio leads to different results. Moreover, the scale of dataset will also be a factor to the results.\n4. I also doubt the evaluation parts since the authors only use GPT-generated samples. so is it reliable? The better way is to make samples by human beings (like PS) to get real photo pairs.", "was_revised": true, "final_justification": "Most of my concerns have been addressed.", "rebuttal": "We sincerely appreciate your positive feedback and comments. Below are our responses to your concerns:\n> W1: About Typos.\n\nWe will correct all typos in the revised manuscript.\n\n> W2: Clarification on how continuous parameter prompting and parameter-aware modulation connect to the problem.\n\nExisting diffusion-based editing frameworks face two key limitations when applied to photorealistic, parameter-controllable image editing:\n\n1. Camera parameters are inherently continuous, whereas pre‑trained diffusion backbones (e.g., SD3) encode textual prompts as discrete tokens through their text encoders. As a result, most diffusion‑based editing methods struggle to capture smooth parameter adjustments, limiting their applicability to photographic editing tasks.\n\n2. Diffusion models lack prior knowledge of camera parameters and fail to capture the corresponding visual effects for each specific parameter.\n\nTo address these limitations, we introduce two complementary modules:\n\n- **Continuous Parameter Prompting** interpolates between semantically meaningful anchor embeddings within the continuous text embedding space. This preserves alignment with representation distribution of the pre-trained model while enabling stable, fine-grained, and perceptually coherent control over camera parameters, as demonstrated (see Sec. 5.2).\n- **Parameter-Aware Modulation** moulates the feature inside the diffusion transformer to explicitly capture spatial and global effects that text embeddings alone cannot represent. It applies spatial warping to model geometry/focus changes and channel‑wise scaling to account for exposure and intensity variations. \n\nBy integrating these two modules, our framework enables stable, realistic, and continuous camera parameter editing. We will refine Sec. 1 to clarify these connections.\n\n>W3: Request for more discussion on the dataset, including the pros and cons of data sources, the effect of data ratios, and the influence of scale.\n\nReal paired data provide faithful camera effects and realistic details, helping the model learn real parameter-dependent visual cues. However, collecting large-scale paired real data is challenging, particularly for diverse conditions such as animals or uncontrolled outdoor scenes. Synthetic data, on the other hand, enables broader coverage of parameter values and scene diversity while ensuring precise annotations.\n\nWe evaluate the impact of these data sources through an ablation study (Table R3-1).  \n- Training only on real data (~7k) results in lower perceptual quality and parameter accuracy due to the limited diversity of real scenes and insufficient parameter coverage.\n- Adding synthetic data, with a 1:1 synthetic-to-real ratio (~15k), consistently outperforms training with real data alone. However, the effect is limited by the overall dataset size.\n- Training only on synthetic data (~40k) improves the image content and parameter control due to its large quantity. However, its visual quality is limited due to the lack of guidance from real captured images.\n- Full CamEdit50K, by combining both available synthetic and real data, achieves better parameter control and visual quality. This improvement is driven by both the quantity of synthetic data and the quality of the real data. As shown in Figure 6 of the main paper, when compared with the rendering-based method (BokehMe) used for our synthetic data, our CamEdit significantly improves visual results, reflecting the effectiveness of combining real and synthetic data.\n\nThese findings demonstrate that both the diversity and scale of the dataset are critical for maximizing performance. We will incorporate these discussions and the ablation results into the revised manuscript.\n\nTable R3-1: Performance of CamEdit with different synthetic-to-real data ratios.\n\n| Task|           **Aperture** |          |          |           **Focal Plane** |          |          |           **Shutter Speed** |          |             |\n|-------------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|   Synthetic:Real | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| 0 : 1     | 4.05         | 0.76  | 1.76    | 4.81            | 0.75  | 0.23    | 4.21               | 0.85  | 0.14    |\n| 1 : 1            | 3.89         | 0.79  | 0.91    | 4.76            | 0.80  | 0.21    | 4.17               | 0.89  | 0.11    |\n| 1 : 0           | 3.91         | 0.82  | 0.66   | 4.93            | 0.76  | 0.15    | 4.19               | 0.87  | 0.12    |\n| **CameEdit50K** | **3.17** | **0.84** | **0.57** | **4.51** | **0.83** | **0.13** | **4.12** | **0.91** | **0.09** |\n\n> W4: Concern about evaluation reliability due to the use of GPT-generated samples.\n\nWe clarify that our evaluation is not based on GPT‑generated samples. All quantitative experiments are conducted on real‑captured images. In addition, we include both a human user study and benchmarks on real datasets with ground‑truth captures to further validate the reliability of our results.\n\n1. **Benchmarks on Real Datasets (Table R3‑2).**  \nWe benchmark CamEdit on three publicly available real datasets containing ground‑truth captures from digital cameras and photographic lenses: VABD [1] for aperture, EBB Val [2] for focal plane, and SICE [3] for shutter speed. As shown in Table R3‑2, CamEdit outperforms both image editing methods fine‑tuned on CamEdit50K (UltraEdit*) and rendering‑based approaches (DrBokeh, CLODE) across SSIM and LPIPS for all tasks.\n\n2. **User Study (Table R3‑3).**\nWe conduct a user study with 15 participants who have expertise in photography. Each participant evaluates 20 image sets per task across all three tasks, scoring photographic realism, parameter accuracy, and content preservation on a 0–4 scale. As shown in Table R3‑3, CamEdit consistently achieves the highest scores across all metrics, confirming that our results remain valid under human evaluation.\n\nThese results demonstrate that our evaluation is grounded in both real-world data and human judgments, ensuring the reliability of our reported performance.\n\nTable R3-2: Quantitative comparison of CamEdit on three real datasets (VABD for Aperture, EBB for Focal Plane, and SICE for Shutter Speed). \n| Task (Dataset)           | Aperture (VABD)             | Focal Plane (EBB Val)           | Task (Dataset)                   | Shutter Speed (SICE)            |\n|---------------------------|------------------------------|----------------------------------|-----------------------------------|----------------------------------|\n| Method                    | SSIM ↑ / LPIPS ↓            | SSIM ↑ / LPIPS ↓                | Method                            | SSIM ↑ / LPIPS ↓                |\n| DrBokeh                   | 0.79 / 0.27                 | 0.79 / 0.24                     | CLODE                             | 0.84 / **0.17** |\n| UltraEdit*                | 0.71 / 0.34                 | 0.73 / 0.31                     | UltraEdit*                        | 0.74 / 0.25                     |\n| **CamEdit (Ours)** | **0.82** / **0.26** | **0.80** / **0.22** | **CamEdit (Ours)** | **0.85** / 0.18                     |\n\nTable R3-3: User Study Scores (0–4 scale) on Photographic Realism, Parameter Accuracy, and Content Preservation\n\n| Task           | Metric                  | UltraEdit / SuperEdit / UltraEdit* / CamEdit  |\n|----------------|-------------------------|-----------------------------------------------|\n| Aperture       | Realism ↑                 | 2.26 / 2.70 / 3.42 / **3.88** |\n|                | Accuracy ↑               | 1.36 / 1.02 / 3.18 / **3.76** |\n|                | Preservation ↑           | 2.10 / 2.76 / 3.38 / **3.87** |\n| Focal Plane    | Realism ↑                | 2.17 / 3.11 / 3.31 / **3.75** |\n|                | Accuracy ↑               | 1.30 / 1.58 / 2.46 / **3.71** |\n|                | Preservation ↑           | 1.80 / 2.83 / 3.14 / **3.81** |\n| Shutter Speed  | Realism ↑                | 2.22 / 3.39 / 3.58 / **3.84** |\n|                | Accuracy ↑               | 1.76 / 1.58 / 2.28 / **3.80** |\n|                | Preservation ↑           | 2.00 / 3.26 / 3.52 / **3.94** |\n\n\n[1] Andrey Ignatov, et al. AIM 2020 Challenge on Rendering Realistic Bokeh. ECCVW, 2020. \n\n[2] Kang Chen, et al. Variable Aperture Bokeh Rendering via Customized Focal Plane Guidance. arXiv, 2024. \n\n[3] Jianrui Cai, et al. Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images. TIP, 2018.\n\n> L: Request to discuss the limitations of the work.\n\nAs mentioned in Section 6 of the main paper, our work already covers some camera parameters, but we plan to extend the approach to include more parameters and editing tasks in future work. We will explicitly outline these plans and limitations in the revised manuscript."}, {"review_id": "TWDlwknTNa", "reviewer": "Reviewer_U816", "summary": "This paper introduces a novel diffusion-based framework designed for photorealistic image editing through continuous control of camera parameters. Two core technical contributions are proposed: a continuous parameter prompting module which generates continuous parameter embeddings and a parameter-aware modulation module which modulates intermediate features conditioned on the camera parameter. Besides, a large dataset CamEdit50K combining real-world photos with estimated parameters and synthetic data rendered with precise ground-truth settings are introduced. Quantitative and qualitative results are provided to demonstrate the model's performance.", "questions": "- Does the three different camera parameters editing require training three different models?\n- Could the author provide more quantitative results on a larger benchmark or testing set?\n- More exploration and discussion about the current disability of inverse recovery are needed.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The paper is well-motivated with a focus on a clear and important limitation in the current state of diffusion-based image editing. \n- The attempt to construct parameter guidance on both parameter prompting and latent modulation is novel.\n- A large dataset CamEdit50k is introduced in this paper, which is contributed to the community.\n- The experimental results show that the proposed method surpasses previous methods.\n\nWeaknesses:\n- As mentioned in line 194, if I understand correctly, the three different camera parameters editing requires training three different models, which makes the method less practical and inefficient in the co-adjustment of different camera parameters.\n- As mentioned in line 208, the evaluation is conducted on 20 images, which weakens the statistical reliability of the result.\n- As mentioned in line 282, the proposed method \"cannot recover focus from blurred regions\". I’d recommend including more in-depth discussions on the current disability of inverse recovery (e.g., is the editing itself an irreversible process, or is it affected by the introduced modulation?).", "was_revised": true, "final_justification": "The authors have addressed most of my concerns about the Joint Parameter and the Benchmark in their rebuttal. Therefore, I maintain my score and recommendation for acceptance.", "rebuttal": "We sincerely appreciate your positive feedback and comments. Below are our responses to your concerns:\n> W1&Q1: Concern about whether editing three camera parameters requires training three separate models.\n\nWe conduct an experiment training a unified model to adjust all three parameters simultaneously. In this setting, we doubled the number of training parameters and incorporated 5,000 synthetic images with multi‑parameter variations. As shown in Table R4‑1, the unified model achieves accuracy and output quality comparable to single‑parameter control, confirming its effectiveness in handling multi‑parameter adjustments. We will include these updated results and further clarify this capability in the revised manuscript.\n\nTable R4-1: Performance of CamEdit with single-parameter vs. joint multi-parameter control, evaluated across 200 images with various parameter settings.\n\n| Task             |           **Aperture** |          |          |           **Focal Plane** |          |          |           **Shutter Speed** |          |          |\n|--------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|                    | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| **CamEdit (Single-Parameter)** | 3.34         | 0.83   | 0.60   | 4.46             | 0.84   | 0.15   | 4.28               | 0.93   | 0.11   |\n| **CamEdit (Joint-Parameter)** | 3.42         | 0.84   | 0.65   | 4.70             | 0.83   | 0.17   | 4.12               | 0.91   | 0.15   |  \n\n> W2&Q2: Request for results on a larger benchmark.\n\nWe expand the testing set to 200 images. As shown in Table R4‑2, CamEdit consistently outperforms all baselines across aperture, focal plane, and shutter speed, achieving superior perceptual quality, semantic consistency, and parameter accuracy on all tasks. These results are consistent with those reported in the main paper and will be incorporated into the revised version.\n\nTable R4‑2: Quantitative comparison of 200 images across aperture, focal plane, and shutter speed.  \n\n| Method             |           Aperture            |          |          |           Focal Plane           |          |          |           Shutter Speed           |          |          |\n|--------------------|-----------------------------------:|---------:|---------:|-------------------------------------:|---------:|---------:|---------------------------------------:|---------:|---------:|\n|                    | NIQE ↓                            | DINO ↑   | Error ↓  | NIQE ↓                              | DINO ↑   | Error ↓  | NIQE ↓                                | DINO ↑   | Error ↓  |\n| UltraEdit          | 4.58                              | 0.70     | ~5.00    | 5.57                                 | 0.74     | ~0.50    | 5.02                                   | 0.72     | ~0.50    |\n| SuperEdit          | 4.43                              | 0.73    | ~5.00    | 5.28                                 | 0.73     | ~0.50    | 4.86                                   | 0.75     | ~0.50    |\n| UltraEdit*         | 4.21                              | 0.78     | 1.87     | 5.14                                 | 0.79     | 0.32     | 4.69                                   | 0.88     | 0.23     |\n| **CamEdit** | **3.34** | **0.83** | **0.60** | **4.46** | **0.84** | **0.15** | **4.28** | **0.93** | **0.11** |\n\n> W3&Q3: More exploration and discussion about the current disability of inverse recovery.\n\nWe clarify that our method—similar to most camera parameter rendering approaches (e.g., DrBokeh [1], MPIB [2]) and image editing frameworks (e.g., SuperEdit [3])—is not designed to restore information that is already absent from the input. CamEdit with the proposed modulation simulates the effects of adjusting camera parameters but does not attempt to reconstruct content that was never captured. This limitation arises because our task focuses on photographic editing, where fidelity to the original content is prioritized over new content generation.\n\nHowever, we recognize the value of exploring inverse image restoration for image editing. Our CamEdit50K pipeline provides a foundation for such research, and we plan to investigate extending our framework for restoring degraded content in future iterations.\n\n[1] Yichen Sheng et al. Dr. Bokeh: Differentiable Occlusion-Aware Bokeh Rendering. CVPR, 2024.\n\n[2] Juewen Peng et al. MPIB: An MPI-based bokeh rendering framework for realistic partial occlusion effects. ECCV, 2022.\n\n[3] Fan Chen et al. SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing. ICCV, 2025."}]

Author rebuttal:

Meta-review: The paper introduces camera-aware image editing, allowing continuous manipulation of camera parameters such as aperture, shutter speed and focal plane. The work introduces a number of techniques on how to incorporate these camera parameters into a diffusion backbone. This work also introduces a new datasets CamEdit50k with different camera settings.

The authors addressed the concerns from the reviewers: evaluated CamEdit on three real-world datasets, added dataset ablations on CamEdit50K (synthetic vs real vs combined), expanded the evaluation set and provided model ablations.

The reviewers agree that the paper is recommended for acceptance.
Decision: Accept (poster)