Title: CamEdit: Continuous Camera Parameter Control for Photorealistic Image Editing
Venue: NeurIPS 2025
Accepted: yes
Keywords: Photorealistic Image Editing

Camera Parameter Control

Diffusion model
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

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 diffusionbased 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.

1 Introduction

Recently, diffusion models [1, 2, 20, 49, 50, 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 become practically valuable. This highlights the need for editing methods that can directly manipulate camera parameters. However, most existing image editing methods [4, 5, 19, 23, 24, 29, 35, 58, 62] focus mainly on three main tasks: semantic editing, stylistic editing and structural editing.

Few prior works target photorealistic image editing, which edits indistinguishable from real photographs through precise control of camera parameters. In this work, we focus on the precise adjustments of focal plane 1 , aperture and shutter speed in camera parameters, which play a fundamental role respectively in determining focal range, background defocus degree, and exposure time [46, 57] during the photo-taking process.

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-toimage (T2I) or text-to-video (T2V) generation frameworks [11, 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 (e.g. ' Adjust the image with aperture f/ 2 . 8 ', etc.). This mismatch hampers smooth parameter adjustment and limits applicability to photographic editing.

To overcome these challenges, we introduce CamEdit , a diffusion-based 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 of 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-specific effects, we further propose a parameteraware 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 a hybrid dataset named CamEdit50K , which includes real-world photographs with extracted or estimated EXIF metadata 2 , 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, our main contributions can be summarized as follows:

We propose CamEdit , a diffusion-based framework for photorealistic image editing that enables continuous and fine-grained control over intrinsic camera parameters such as aperture, focal plane, and shutter speed, entirely through manual textual prompts.

Wedesign a continuous parameter prompting mechanism and a parameter-aware modulation module to enable smooth and physically consistent control across varying camera settings.

Weconstruct a dataset, CamEdit50K , which contains aligned image pairs and corresponding camera parameter instructions, addressing the lack of supervised data for photorealistic image editing with continuous camera parameter.

2 Related Work

Image Editing with Diffusion Models. Recent diffusion-based generative methods such as Imagen [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 a 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-Prompt [19] manipulates cross-attention 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 [17] and MGIE [13], 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 such as masks and bounding boxes [58, 24, 4] to better preserve background consistency. Other efforts explore multi-task instruction tuning on large-scale synthetic datasets [37, 69, 64], enabling finer-grained semantic control. Approaches with sliders [16, 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-Aware Models. Traditional camera-aware editing methods have shown strong performance in tasks such as focal plane adjustment [57, 45, 46, 63] and aperture simulation [7, 56]. These methods are typically based on physically inspired image formation models and often require additional inputs such as depth maps or aperture geometry [57, 45, 46, 18, 61, 63]. However, their applicability is generally limited to single-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 focusing on extrinsic parameters like pose, viewing angle [8, 21, 33], or motion trajectory in text-to-video generation [40, 65, 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 [11, 12] and T2V [70] 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-aware datasets [11, 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 CamEdit50K, a dataset specifically designed for photorealistic image editing under continuous, physically grounded camera control.

As shown in Table 1 and Figure 2, CamEdit50K unifies paired real and synthetic imagery, multiparameter 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 a real-data parameter estimation pipeline. Synthetic images are produced with a synthdata 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 camera-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 is 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) Aperture: 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 a simplified thin-lens model [44]. (iii) Shutter Speed: We estimate

Focal Plane

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2600

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ol

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 as 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 of images for each parameter in CamEdit50K.

exposure time from global brightness statistics and invert the camera response using a differentiable ISP pipeline [9, 31, 36], yielding a 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-field effects, we employ a differentiable bokeh renderer [57]. (ii) Aperture: Using the depth map from [67], we fix the focal plane on the foreground 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 f/ 10 , 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 to 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 as illustrated in Figure 3. Given a camera-parameter instruction, we 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-aware modulation modules described in Section 4.2, which inject parameter-specific 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, as shown in Section 5.3. To address this, our continuous parameter prompting synthesizes parameter representations by interpolating between anchor embeddings of adjacent discrete tokens within the text embedding space. The

'

Adjust the image

with aperture f/<P>.

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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 diffusion-based frameworks.

Let p ∈ R denote a continuous camera parameter, and { p 1 , . . . , p K } be a set of predefined discrete anchor values with associated learnable embeddings { e 1 , . . . , e K } ⊂ R d , where each e k is obtained by encoding the anchor token via the frozen CLIP tokenizer and text encoder. For any p ∈ [ p i , p i +1 ] , the parameter embedding is computed as:

where ϕ ( p ) ∈ [0 , 1] represents the normalized relative position of p between anchors p i and p i +1 . The linear projection aggregates the semantic content of the two neighboring embeddings, while the MLP, implemented as a two-layer feed-forward network with ReLU activation, introduces a position-dependent residual to capture fine-grained variation. The final embedding e p replaces the parameter placeholder in the encoded text prompt representation.

4.2 Parameter-Aware Modulation

Camera parameter variations primarily influence the spatial appearance of an image while preserving its underlying semantic content. Such changes include spatial transformations, 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.

To effectively model visual effects, we modulate intermediate features conditioned on the parameter p through two complementary operations as shown in Figure 4. To improve parameter sensitivity and information flow, the modulation is applied after the self-attention layers in each

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transformer block, where features contain rich contextual dependencies. The first component, geometry-aware spatial modulation, models lens-induced geometric distortion and depth-dependent focus transitions. It predicts a spatial displacement field that perturbs feature coordinates based on visual features and the input parameter. Specifically, we apply 2 × 2 average pooling to the input feature map F and feed the pooled representation, together with p , into a parameter-adaptive MLP:

where AvgPool ( · ) denotes average pooling over non-overlapping 2 × 2 patches. The warped feature is then computed via:

where G base 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:

Both MLPg and MLPp are implemented as single-layer feed-forward networks with intermediate ReLU activation. Then F p is forwarded to the subsequent transformer block.

5 Experiments

5.1 Implementation Details

Training and Inference. We adopt Stable Diffusion 3 (SD3) [10] as our backbone due to 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 [72], and we fine-tune lightweight adapters using LoRA [22], combined with our physics-driven adaptation module. We train the model using AdamW [38], with learning rates of 1e-5. Training is conducted on 512 × 512 resolution images with a 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.

Metrics. We evaluate performance from three perspectives: perceptual quality, content preservation, and parameter control accuracy. Perceptual quality is measured using NIQE [42] 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 error between the instruction-specified 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-Art 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], as well as the camera-aware I2V model PhotoGen [70]. To evaluate PhotoGen [70], we generate images using prompts sampled from GPT-4o to simulate realistic text-based generation requests. We retrain UltraEdit on our CamEdit50K to enable camera-aware editing, denoted as 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, we include advanced low-light and exposure-aware methods such as SCI [39], CycleR2R [36], and CLODE [26]. All methods are tested under a consistent exposure configuration and evaluated for their ability to adapt image brightness while preserving both structural integrity and perceptual quality.

Quantitative Comparison. As shown in Table 2 (a), our method consistently outperforms other editing across all tasks in NIQE, DINO, and control error. Compared to the retrained UltraEdit* 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 to generate parameter-consistent results. Although UltraEdit* benefits from CamEdit50K supervision, it 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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(a) Diffusion-Based Methods

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photographic realism, content preservation, and parameter accuracy. As shown in Figure 5, our method achieves the highest average scores on all dimensions.Relative 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 baselines on most metrics, such as DrBokeh [57], BokehMe [45] on both aperture and focal plane editing tasks. For aperture editing, our 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 diffusionbased editing models lack such a 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, our method ensures smooth transitions and 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 editing-based formulation and the use 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 or exposure refinement that enhances visual aesthetics.

Figure 7 further supports our findings. Under aperture variation, our method preserves fine details like hair strands and object contours. For focal plane editing, we maintain sharpness in in-focus areas,

p = 5.6

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Ours

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p = 0.115

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PhotoGen

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Additional Results (Ours)

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f/3.5

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DrBokeh

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BRVIT

BokehMe

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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 are conducted on the focal-plane task.

(a)

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Continuous Parameter Prompting. As shown in Table 3, removing continuous parameter prompting (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 6.

Wefurther 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 8 (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 to adapt feature representations accordingly. As shown in Figure 8 (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 is higher by 0.30, DINO is lower by 0.03, and Error is higher by 0.09, indicating weaker parameter control due to the absence of localized spatial modeling.

Composition of CamEdit50K. We analyze CamEdit50K by varying the ratio of synthetic to real data, as shown in Table 4. Training on real data alone lacks scene diversity and parameter coverage, leading to weaker perceptual quality and control. Adding synthetic data at 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, a 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 is 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 a valuable direction for future research.

6.1 Acknowledgments

This work was supported in part by the Shenzhen Science and Technology Program (No. KQTD20221101093559018), the National Natural Science Foundation of China (No. 62025604), and the CIE-Smartchip 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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NeurIPS Paper Checklist

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The reviewers of your paper will be asked to use the checklist as one of the factors in their evaluation. While "[Yes] " is generally preferable to "[No] ", it is perfectly acceptable to answer "[No] " provided a proper justification is given (e.g., "error bars are not reported because it would be too computationally expensive" or "we were unable to find the license for the dataset we used"). In general, answering "[No] " or "[NA] " is not grounds for rejection. While the questions are phrased in a binary way, we acknowledge that the true answer is often more nuanced, so please just use your best judgment and write a justification to elaborate. All supporting evidence can appear either in the main paper or the supplemental material, provided in appendix. If you answer [Yes] to a question, in the justification please point to the section(s) where related material for the question can be found.

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Answer: [Yes]

Justification: The abstract and introduction in Section 1 clearly state the main claims.

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The answer NA means that the abstract and introduction do not include the claims made in the paper.

The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers.

The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.

It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.

2. Limitations

Question: Does the paper discuss the limitations of the work performed by the authors?

Answer: [Yes]

Justification: Please refer to Section 6.

Guidelines:

The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.

The authors are encouraged to create a separate "Limitations" section in their paper.

The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.

The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.

The authors should reflect on the factors that influence the performance of the approach. For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.

The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.

If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness.

While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.

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Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?

Answer: [NA]

Justification: The paper does not include theoretical results.

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The answer NA means that the paper does not include theoretical results.

All the theorems, formulas, and proofs in the paper should be numbered and crossreferenced.

All assumptions should be clearly stated or referenced in the statement of any theorems.

The proofs can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged to provide a short proof sketch to provide intuition.

Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.

Theorems and Lemmas that the proof relies upon should be properly referenced.

4. Experimental result reproducibility

Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?

Answer: [Yes]

Justification: We clearly describe the implementation details of our method in Section 5.

Guidelines:

The answer NA means that the paper does not include experiments.

If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.

If the contribution is a dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.

Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.

While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example

(a) If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm.

(b) If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully.

(c) If the contribution is a new model (e.g., a large language model), then there should either be a way to access this model for reproducing the results or a way to reproduce the model (e.g., with an open-source dataset or instructions for how to construct the dataset).

(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reproducibility. In the case of closed-source models, it may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers to have some path to reproducing or verifying the results.

5. Open access to data and code

Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?

Answer: [Yes]

Justification: The code and data will be made publicly available upon acceptance of the paper.

Guidelines:

The answer NA means that paper does not include experiments requiring code.

Please see the NeurIPS code and data submission guidelines ( https://nips.cc/ public/guides/CodeSubmissionPolicy ) for more details.

While we encourage the release of code and data, we understand that this might not be possible, so 'No' is an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for a new open-source benchmark).

The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines ( https: //nips.cc/public/guides/CodeSubmissionPolicy ) for more details.

The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.

The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why.

At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).

Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted.

6. Experimental setting/details

Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?

Answer: [Yes]

Justification: We clearly describe the training and test details of our method in Section 5.

Guidelines:

The answer NA means that the paper does not include experiments.

The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them.

The full details can be provided either with the code, in appendix, or as supplemental material.

7. Experiment statistical significance

Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?

Answer: [No]

Justification: We do not report error bars because our method demonstrates consistently significant improvements, as shown in Table 1 and Table 2. Additionally, the scale of experiments and associated computational cost make repeated trials impractical.

Guidelines:

The answer NA means that the paper does not include experiments.

The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.

The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions).

The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)

The assumptions made should be given (e.g., Normally distributed errors).

It should be clear whether the error bar is the standard deviation or the standard error of the mean.

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified.

For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates).

If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.

8. Experiments compute resources

Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?

Answer: [Yes]

Justification: The detailed description is presented in Section 5.

Guidelines:

The answer NA means that the paper does not include experiments.

The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.

The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute.

The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).

9. Code of ethics

Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https://neurips.cc/public/EthicsGuidelines ?

Answer: [Yes]

Justification: We have carefully checked the Ethics Guidelines to make sure our research is with it

Guidelines:

The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.

If the authors answer No, they should explain the special circumstances that require a deviation from the Code of Ethics.

The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction).

10. Broader impacts

Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?

Answer: [Yes]

Justification: Please refer to Section 6.

Guidelines:

The answer NA means that there is no societal impact of the work performed.

If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact.

Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.

The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.

The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.

If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).

11. Safeguards

Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?

Answer: [NA] .

Justification: This paper poses no such risks.

Guidelines:

The answer NA means that the paper poses no such risks.

Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.

Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.

We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort.

12. Licenses for existing assets

Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?

Answer: [NA]

Justification: We do not use existing assets.

Guidelines:

The answer NA means that the paper does not use existing assets.

The authors should cite the original paper that produced the code package or dataset.

The authors should state which version of the asset is used and, if possible, include a URL.

The name of the license (e.g., CC-BY 4.0) should be included for each asset.

For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.

If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, paperswithcode.com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of a dataset.

For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.

If this information is not available online, the authors are encouraged to reach out to the asset's creators.

13. New assets

Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?

Answer: [Yes]

Justification: We introduce a new dataset, CamEdit50K, for camera-aware image editing with continuous parameter annotations. The dataset includes both real and synthetic image pairs with aligned camera parameter instructions.

Guidelines:

The answer NA means that the paper does not release new assets.

Researchers should communicate the details of the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.

The paper should discuss whether and how consent was obtained from people whose asset is used.

At submission time, remember to anonymize your assets (if applicable). You can either create an anonymized URL or include an anonymized zip file.

14. Crowdsourcing and research with human subjects

Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well as details about compensation (if any)?

Answer: [NA]

Justification: This work does not involve crowdsourcing or research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Including this information in the supplemental material is fine, but if the main contribution of the paper involves human subjects, then as much detail as possible should be included in the main paper.

According to the NeurIPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.

15. Institutional review board (IRB) approvals or equivalent for research with human subjects

Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?

Answer: [NA]

Justification: The paper does not involve crowdsourcing nor research with human subjects.

Guidelines:

The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.

We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.

For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.

16. Declaration of LLM usage

Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality of the research, declaration is not required.

Answer: [NA]

Justification: GPT-4o was only used for evaluating editing results. It is not a component of our core methodology and does not affect the scientific rigor or originality of the research.

Guidelines:

The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.

Please refer to our LLM policy ( https://neurips.cc/Conferences/2025/LLM ) for what should or should not be described.

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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?).\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nstrengths:\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.\n\nQUESTIONS:\nI d like the authors to address the concerns about the dataset settings and evaluation methods part in the ''weakness'' part.\n\nLIMITATIONS:\nThe authors should discuss limitations about the work.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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.\n\nQUESTIONS:\nPlease see the weaknesses section.\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nCamEdit: 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrength:\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.\n\nQUESTIONS:\n- 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.\n\nLIMITATIONS:\nModel limitations are adequately discussed, the societal impact of manipulations of photographic material should probably be added.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-01"}