Title: Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
Venue: NeurIPS 2025
Accepted: yes
Keywords: Photography Composition

Photography Perspective Composition
Abstract: Traditional photography composition approaches are dominated by 2D cropping-based methods. However, these methods fall short when scenes contain poorly arranged subjects. Professional photographers often employ perspective adjustment as a form of 3D recomposition, modifying the projected 2D relationships between subjects while maintaining their actual spatial positions to achieve better compositional balance. Inspired by this artistic practice, we propose photography perspective composition (PPC), extending beyond traditional cropping-based methods. However, implementing the PPC faces significant challenges: the scarcity of perspective transformation datasets and undefined assessment criteria for perspective quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) A video generation approach that demonstrates the transformation process from less favorable to aesthetically enhanced perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires no additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel approach to improving photography composition, referred to as Photography Perspective Composition (PPC). Unlike traditional methods that rely on 2D cropping, PPC enhances the composition by adjusting the 3D spatial relationships between subjects in the scene while maintaining their actual positions. The core idea is to provide users with a more intuitive and dynamic method for improving compositions through before-and-after perspective transformation comparisons in video form. This allows users to visually understand how changes in perspective can improve the composition.\nIn addition, the paper outlines an automated framework for generating perspective transformation datasets using expert photographs, addressing the challenge of acquiring large-scale perspective transformation data. \nFinally, the authors present a Perspective Quality Assessment (PQA) model, designed to evaluate the quality of perspective transformations across three dimensions: visual quality, motion quality, and composition aesthetics.\nThis framework provides a user-friendly tool for ordinary users to improve their photography skills by learning how to adjust compositions based on perspective transformations. The method bridges the gap between professional photographers’ techniques and everyday users by offering an accessible way to enhance compositional quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. Innovative Approach to Photography Composition: The PPC pipeline presents a novel method that transcends traditional cropping techniques by leveraging 3D perspective adjustments. This approach enables more sophisticated compositional changes, particularly in complex scenes where simple 2D cropping is insufficient. By focusing on adjusting the spatial relationships between objects while preserving their actual positions, the method offers a more holistic solution for enhancing image composition.\n\n2. Dataset Construction: Authors introduce a new dataset using existing professional-level aesthetic image collections combined with a 3D reconstruction approach. As they propose a new paradigm for photography composition, the need for a well-structured dataset becomes crucial. In this context, their dataset construction makes a significant contribution to advancing the field and offers valuable resources for further developments in both photography composition and computer vision.\n\n3. Supplementary Resources: The authors provide high-quality supplementary materials that effectively demonstrate the results of their pipeline. These additional resources, including video examples, further illustrate how their method performs in practice, offering valuable insights for both researchers and practitioners interested in the proposed approach.\n\nWeakness\n\n1. Lack of Clear Explanation: While the paper introduces several components that appear reasonable, it lacks clear, detailed explanations in several key areas. First, for the dataset, the paper does not specify how much data was collected and used, nor does it mention how the train and validation sets were composed. Second, regarding the use of guidance, the paper mentions the use of red bounding boxes and feature matching along with homography transformations as key components, but it does not provide sufficient details on how these methods are integrated into the overall learning process. Furthermore, the paper introduces RLHF (Reinforcement Learning with Human Feedback) but does not clarify the prompts used or define what constitutes higher-quality and lower-quality videos. Additionally, it is unclear which model was used as `ours' in the experiments. For example, while the Wan2.1 14B model is marked in gray in the table, it is not clearly identified as the model referred to in the experiment section.\n\n2. Validity of PQA Model: The paper leverages a VLM-based approach for quality assessment, which is understandable. However, many other methods demonstrate the validity of their VLMs by showing how their outputs align with true human evaluations. While the paper reports scores in Table 2, it lacks details on the number of annotators involved in rating the videos. Additionally, if the ratings come solely from expert annotators, their reliability may be heavily biased toward their personal preferences, raising concerns about the generalizability of the results. The paper does not discuss how the model's results can be validated by broader user agreement or how inter-annotator reliability is ensured.\n\n3. Concerns Regarding Changes in Visual Attributes: The results presented in the paper indicate that visual attributes such as exposure, color, and contrast are also altered during perspective transformations. While these changes are understandable, given their dependence on the I2V models, they may also impact human preferences during evaluation. The paper does not address how these visual changes might affect the subjective quality assessments of users. A more thorough explanation is needed on whether the proposed pipeline accounts for such changes, or if there are potential solutions to minimize their impact on the evaluation.\n\n4. Minor Weaknesses:\nOn Line 120, the paper mentions \"trajectory\" and refers to the Discussion section, but no relevant discussion on this topic appears later in the paper.\nOn Line 219, the paper states that a linear projection head is shared across dimensions. However, it is unclear why this is the case. Intuitively, if the model outputs different rewards, it would make sense for the projection head to vary for each reward type.\nOn Line 166, the paper mentions a 'reference model.' I assume this refers to the initially trained PPC model, but the paper does not provide enough detail to confirm this. It would be helpful if the authors could clarify what is meant by 'reference model' and specify whether this refers to the trained PPC model or another model used in the experiment.\n\nQUESTIONS:\n1. Can the authors provide more detailed information on the size and composition of the dataset, including the train-validation split? Additionally, would the authors be willing to release the dataset as open-source?\n2. How are the red bounding boxes, feature matching, and homography transformations integrated into the overall learning process?\n3. Could the authors clarify what is meant by the 'reference model' in Line 166 and specify which model is used in the experiments?\n4. How were human preferences gathered, and how did the authors ensure inter-annotator reliability in the PQA evaluation?\n5. How does the proposed pipeline account for or minimize the impact of visual attribute changes (e.g., exposure, color, contrast) during perspective transformations?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-18"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper studies the problem of photo composition improvement. The key idea is to formulate photo recomposition as perspective adjustment,  instead of 2D cropping as in traditional methods.\n\nThe contributions of the paper are threefold: 1) an automatic pipeline for building a perspective dataset for perspective-based photo recomposition; 2) a video-based method to transform input photos towards optimal perspectives; 3) a model for automatic assessment of perspective transformation quality.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n\n1. The idea of photo recomposition through perspective transformation is novel and interesting, which brings a new perspective to aesthetic photo composition recommendation and has potential to inspire the community.\n\n2. The proposed perspective-based recomposition model (Section 3.3) and perspective quality assessment model  (Section 3.4) are well motivated, designed, and technically solid.\n\n3. The results shown look promising.\n\nWeaknesses\n\n1. Comparison to state-of-the-art cropping-based methods is missing. This experiment is important to support the value and importance of the claimed advantages of the perspective-based recomposition (as shown in Figure 1). It can be done through a user experiment to investigate if perspective-based recompositions are really preferred by users over cropping-based recompositions.\n\n2. The practical applicability of the proposed approach is questionable. The proposed recomposition formulation is synthesis-based, and thus would suffer from inherent limitations, such as synthesis artifacts (e.g., slight structure distortion as in the background of the final perspective in 2nd row of Figure 5) and input-output content inconsistency (e.g., the sleeve wrinkles in the input perspective disappear in the final perspective in 1st  row of Figure 5). In contrast, the cropping-based paradigm does not have these issues. These limitations would limit the merit of the proposed method in real image editing tasks where high-fidelity edited results are desired.\n \n3. Comparison of the proposed video-based approach (Section 3.3) to an image-based approach that directly transforms the input perspective to the optimal perspective ( without generating the intermediate transformation process) is missing, which is needed to more thoroughly show the necessity of the video-based approach (e.g., if it can achieve better performance than the image-based approach).\n\nQUESTIONS:\nIn Section 3.2,  is the camera motion trajectory for obtaining the suboptimal perspective generated randomly? Has any more sophisticated strategy of generating camera motion trajectories been considered?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-30"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a new approach to enhancing photo composition by adjusting perspectives, moving beyond traditional cropping methods. The authors propose three main contributions: an automated framework for constructing perspective composition datasets using expert photos, a video generation method that demonstrates perspective adjustments from suboptimal to optimal views, and a Perspective Quality Assessment (PQA) model that evaluates perspective transformations based on visual quality, motion quality, and composition aesthetics. These contributions aim to provide users with intuitive guidance on improving their photo composition skills through perspective adjustments, making professional-level composition more accessible.\n\nSTRENGTHS AND WEAKNESSES:\n#### Strengths\n- The paper introduces the novel concept of PPC. This research offers users a novel and intuitive way to enhance photo composition skills. By demonstrating dynamic perspective adjustments, it helps users more easily understand the optimization of viewpoints in professional photography, making high-quality composition more accessible.\n- A well-designed pipeline is proposed to synthesize suboptimal-to-optimal perspective video pairs from high-quality photographs via 3D reconstruction and video inversion. This method is scalable and avoids the need for manual video collection.\n- The authors introduce a PQA model trained in two stages (unpaired and paired), enabling automatic filtering of noisy samples and use as a reward signal in RLHF. It evaluates videos based on visual quality, motion quality, and composition aesthetics.\n- The paper presents extensive quantitative and qualitative experiments across various scenarios (single-subject, multi-subject, landscape, UAV-like views), with a thoughtful design of perspective accuracy and aesthetic metrics.\n\n#### Weaknesses\n- The method's effectiveness is mostly validated through expert annotations and model-based scores. No user studies or crowdsourced evaluations are conducted to verify usability or alignment with amateur users’ preferences.\n- The performance degrades when training data is scaled beyond a certain volume, as shown in Tab. 2c. This suggests the model struggles with maintaining perspective consistency and compositional reasoning under data diversity.\n- The CA metric involves many subjective sub-dimensions, making it prone to annotator bias. The paper does not report inter-annotator agreement, so the reliability and objectivity of this evaluation remain unclear.\n- A major concern is whether the proposed model can be effectively used in real-world scenarios. While the technical contributions are promising, the paper currently lacks evidence of practical usability. Without some form of user validation or deployment case study, it remains unclear how well the system performs when used by its target audience.\n\nQUESTIONS:\n- The performance drop observed in Tab. 2c with increased data scale may stem not necessarily from model design limitations, but potentially from issues such as distributional mismatch, reward sparsity, or instability in RLHF optimization. Could the authors provide deeper analysis—e.g., gradient variance, sample entropy, or reward fluctuation curves—to better diagnose the training instability? \n- Given that the method is intended to aid amateur users in improving photographic composition, even a lightweight usability assessment (e.g., a small-scale pilot study or qualitative feedback from non-expert participants) could significantly strengthen the claim of practical value. Furthermore, users typically care more about the final result than the quality of the guidance video itself. It may therefore be helpful to compare the aesthetic scores of the first and last frames to show whether the model effectively improves composition over time.\n- While PQA and RLHF improve training robustness by filtering low-quality data, this may reduce the model’s exposure to difficult inputs. In real-world use, users may still provide blurry or poorly composed photos. We suggest evaluating the model’s performance on such low-quality inputs to ensure practical robustness.\n- The paper lacks details on computational cost and runtime, making it unclear if the method suits real-time or everyday use. \n- When emphasizing the innovation of PPC, the paper could provide a more detailed comparative analysis of PPC with existing cropping-based composition methods and other related technologies in practical application scenarios.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes PPC, photography perspective composition, a model that can enhance the aesthetics of perspective in photography. The main idea is using a video generator to \"warp\" photographs with \"suboptimal\" perspective towards an aesthetically more pleasing composition, e.g., with the subject in the middle. The authors overcome the data scarcity problem for this approach by creatively \"destructing\" ground truth data (well-composed photographs, of which there are millions available) and then having the generator learn the inverse of that transformation. Overall, the paper is well written and executed, and the results look promising.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths** \n- The idea is interesting and novel. I appreciate the authors branching out into the field of perspective composition by leveraging a video model, and thus being able to go beyond 2D-crop-based approaches. As the quality of video models will advance, so will the performance of this technique, which is why I think that this is an interesting and important paper. \n- The paper leverages several existing techniques that have been proven to work well on different applications and in different contexts, e.g., RLHF, DPO, etc. As such, it is well grounded in the literature. \n- I appreciate the detailed supplemental and the provided videos. \n\n___\n\n**Weaknesses**\n- The current PQA method seems empirical, but will hopefully become less and less important as video generators keep improving in quality (see the first point under strengths). \n- I feel that the fact that the model sometimes hallucinates / changes small details, such as the leaves in Fig. 7, can change the essence of the photograph slightly, which might be undesired for certain scenarios, e.g., portrait / wedding / pet photography. I'm wondering whether this will be a problem for close-ups of faces. \n- In the supplemental video of the Taj Mahal, the PPC seems to overshoot the best perspective a bit. Have the authors experience similar issues, and is there a straightforward fix for this? \n\n___\n\n**Writing:**\n- Please be careful with the word \"optimal\", especially when referring to sth as subjective as perspective (e.g., L. 13, 30, 33), since none of these are objectively, provably optimal. \n- Starting a sentence with \"While\" usually implies a contradiction to follow, for example: While this seemed like a great idea initially, it later turned out to be wrong. Please adjust accordingly, see L. 21, 25, etc... \n- A reference to cite for the related work section \"Evaluation with Human Performance\" is NICER: Aesthetic image enhancement with humans in the loop, by Fischer et al.\n\nQUESTIONS:\nsee above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}