Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
Authors: Lujian Yao
Siming Zheng
Xinbin Yuan
Zhuoxuan Cai
Pu Wu
Jinwei Chen
Bo Li
Peng-Tao Jiang
Venue: NeurIPS 2025 poster ()
Keywords: Photography Composition
Photography Perspective Composition
Primary area: applications

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.

Photography Perspective Composition: Towards Aesthetic Perspective Recommendation
Lujian Yao" Siming Zheng
Zhuoxuan Cai Bo Li
Xinbin Yuan
Peng-Tao Jiang Pu Wu
Jinwei Chen
vivo Mobile Communication Co., Ltd
Project page: ecust edu, en, pt jt. com
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 adjust- ment as a form 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 meth- ods. However, implementing the PPC faces significant challenges: the scarcity perspective transformation datasets and undefined assessment criteria for perspec- tive quality. To address these challenges, we present three key contributions: (1) An automated framework for building PPC datasets through expert photographs. (2) video generation approach that demonstrates the transformation process from less favorable aesthetically enhanced perspectives. (3) A perspective quality assessment (PQA) model constructed based on human performance. Our approach is concise and requires additional prompt instructions or camera trajectories, helping and guiding ordinary users to enhance their composition skills.
Introduction
Professional photography demands expertise in multiple aspects, with photographic composition being one of the most crucial. Photographic composition refers to the arrangement of visual elements according to aesthetic principles. It requires photographers to harmoniously integrate multiple elements like people, urban, and natural features. Master photographers, such those Magnum Photos, require professional knowledge and extensive training, making quality photography expensive and challenging for ordinary people. This raises the question: Can we help ordinary people achieve professional-leve composition?
Traditional photography composition approaches are primarily based on cropping. Numerous ap- proaches have been developed for image cropping, including saliency-based methods [36], learning-
based techniques [5] g 20 26
27
42 5T] and reinforcement learning strategies [19] However,
crop-based methods are inherently limited, they only allow for 2D recomposition within the image plane. As shown in Fig. Ta. traditional crop-based methods primarily focus on learning crop template. However, when the scene itself is chaotic and lacks good compositional structure, cropping alone rarely produces satisfactory results.
"Itern at vivo Mobile Communication Co., Ltd. Prooecct lead.
Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Perspective Composition Recommendation
Crop Templates
(a) Crop-based Photo Composition
(b) Our Perspective-based Photo Composition
Figure I: The motivation the proposed photography perspective composition (PPC). Traditional crop-based methods (a) focus on learning crop templates for better composition. However, when scenes contain chaotic arrangements of subjects, cropping alone rarely yields satisfactory results. Perspective transformation (b) addresses these challenges by adjusting spatial relationships between subjects (e.g., person and tree, red arrow) and scene orientation.
In real-world scenarios, photographers address these limitations of 2D cropping by actively adjusting their perspective and positions to achieve improved spatial relationships between subjects. Through perspective adjustments, photographers can create sophisticated compositions by systematically arranging subjects within the frame, manipulating spatial relationships to create dynamic and engaging images. Fig. [p illustrates how perspective transformation can address compositional challenges.
Inspired by this artistic practice, we introduce photography perspective composition (PPC) as a new paradigm for photography composition. However, implementing PPC presents three main challenges: Data acquisition is particularly challenging as currently available datasets are limited to planar crop data and lack perspective transformation information. @ The implementation of perspective recommendation requires careful design considerations, as compositional aesthetics often follow partial ordering rather than total ordering relationships. The aesthetic evaluation of different perspectives requires new metrics and evaluation methods.
To address these challenges, propose three key solutions. First, develop novel method for constructing aesthetic perspective transformation datasets, with an automated data generation pipeline (for ). Second, we implement perspective transformation video generation approach instead of single-image recommendations. This enables before-and-after compositional comparisons while providing users with intuitive visual guidance (for @). Finally, construct comprehensive perspective quality assessment (PQA) model that evaluates perspective transformation quality through three critical dimensions: visual quality, motion quality, and composition aesthetic (for 3).
Our main contributions can be summarized as follows:
To the best of our knowledge, we are the first to introduce photography perspective position, moving beyond traditional cropping methods. We hope our work can inspire more researchers to explore and advance perspective-basee baseed composition techniques in computational photography.
We develop concise PPC, without requiring additional prompt instructions camera motion trajectories, which can help ordinary users improve their photo composition skills.
We present automated framework for constructing aesthetic perspective transformation datasets, which leverages large-scale expert photograph collections to learn generalizable principles of aesthetic composition.
A perspective quality assessment (PQA) model is constructed based on human performance evaluate the quality of perspective transformation through three aspects: visual quality, motion quality, and compositional aesthetics.
2 Related Work
This section reviews two key research areas related to our work: (1) Photography composition, focusing on various image cropping techniques and their data-driven approaches (Sec. 2.1. and (2)

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the evaluation with human performance, particularly the recent adoption of vision rision-language models (VLM) for quality assessment (Sec. 2.2
2.1 Photography Composition
Prior photography composition methods primarily rely on image cropping. There are three types.
(I) free-form cropping- Numerous techniques have been explored to tackle this problem, including
saliency maps [3). learning-based methods [5] g 11
20
26
27
36 42 [5]], and reinforcement
learning [9]. (2) Subject-aware image cropping 12
where an additional subject mask is
provided indicate the subject of interest. (3) Ratio-aware cropping [4, where the crops
expected to adhere to a specified aspect ratio. Several datasets have been established, including GAICD [5]] CPC 42), FCDB B3, and SACD 47. Recent advances in diffusion models [32 [54] have further expanded the possibilities, with works leveraging Stable Diffusion for synthetic data generation [3 35 and developments in outpainting [22 34 49). However, cropping methods share a fundamental limitation as they operate only in 2D space, making them insufficient when the spatial arrangement of the scene is less favorable.
2.2 Evaluation with Human Performance
Evaluation models are essential for aligning generative models with human preferences. Traditional metrics like FID I0 and CLIP scores
(29 have been widely used [14
15 25 To improve evaluation 53 have evolved from simple metrics to learning-
accuracy, recent works 7 approaches that leverage human preference datasets to train CLIP-based models. Recently, researchers have begun exploring vision-language models (VLMs) 1 40 as more powerful framework for reward modeling. These VLM- approaches have shown success in both evaluation
16 22
43
46
43 44
and optimization [18, 228 38 46), utilizing methods like point-wise regression (9 45 pair-wise comparison with Bradley-Terry loss [2], and instruction tuning 21
leverage reasoning
capabilities for VL.Ms. However, VLMs need substantial data for effective training, yet expert compositional data scarce and costly to obtain, making it challenging to train VLMs capture sophisticated aesthetic principles using only limited expert annotations.
3 Methodology
3.1
Overview
Unlike previous photography composition methods that primarily rely on cropping, we propose novel approach that recommends photography composition through perspective transformation. First, we describe how to construct the dataset and implement an automated pipeline (Sec. B.2). Then, we present the core implementation methods of our PPC, and incorporate RLHF to make the generated videos aligned with human performance (Sec. 3.3) Finally, we introduce the implementation of the perspective quality assessment (PQA) Model (Sec. 3.4
3.2 Automated Construction of PPC Dataset
Currently, no dedicated dataset exists for PPC setting. To promote this area, we propose novel approach for constructing PPC dataset in an automated way. The main challenge lies collecting real-world camera movement sequences that transition from less favorable to well-composed perspective. The closest existing data comes from photographers sharing point-of- view (POV) recordings of their shooting process on streaming platforms. However, these videos typically captured from secondary angles using GoPro, which differs from the main camera perspective. We observe that expertly composed photographs are readily available, and recent advances in single-image scene reconstruction have shown remarkable results [50]. This inspired us to explore an alternative approach: generating camera movement sequences that transition from well-composed to less favorable perspective based existing expertly composed photographs. By reversing these sequences, we can obtain the desired data.
[Intuition.]
[Detail.] (1) Data Source. We select multiple professional photography datasets, including datasets used existing composition studies such as GAICD (SI, SACD 47], FLMS (6) and FCDB (3) Furthermore, expand our data volume, we incorporate Unsplash hhtpp::/ /wwppuuusspsh../eeeeeeeeee com),

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Professional Photographer Images
PPC Dataset Generation
(b) Training PQA Model
Step I. Uxppaleen Fidess
Polnt Cloud Fauma Edinstiun
Alle
Deftision Inporting
Sopp Paired Vilvor
MQ
Pram
Frame
Sunglee
Framn N
Frme Nel
Ramml
Perspective Quality Amreument
Suboptimal to Optimal
Training Data
[428 AP7 7,57
Seary
Projectiun
Perspective Video Generation
Figure 2: Architecture illustration of PPC dataset generation and the training perspective quality assessment (PQA) model.
Table 1: Grade assessment in data filtering.
Grade
Level
Raw Score Range
Standardized Score (Range)
Quality Assessment
A
Excellent
2 5.0 0.0 to 4.9
95 (90-100)
Superior quality
Good
85 (80-89)
Above-average quality
Satisfactory
-5.0 to -0.1
75 (70-79)
Acceptable quality
DDD
Marginal
-15.0
65 (60-69)
Below-average quality
E
Unsatisfactory
15.0
50 (< 60)
Inadequate quality
currently the largest open-source professional photography dataset. (2) Perspective Transformation Generation. As shown in Fig. [ we adopt 3D reconstruction approach. Our 3D reconstruction methodology mainly builds upon the ViewCrafter (50). The inputs consist of well-composed image and a specified camera motion trajectory. Note that this trajectory can be random (refer to [Discussion).. By following this trajectory, we can generate video sequence transitioning from the well-composed to less favorable perspective. Then, by reversing this video sequence, we obtain our desired training data. (3) Data Filtering. Given the limited performance of current reconstruction models, the generated video data needs to filter out artifacts including distortion, fixedness, and blur effects, as depicted in Fig. 2 However, manual filtering for such large dataset is impractical. Our tests showed that a single person can only filter about 3K vidcos per day, making it difficult to process large-scale samples. With the rapid ad advancement of vision language models (VLMs) in scene understanding and automated evaluation [24 28 44) we develop a perspective quality assessment (PQA) model to filter the generated data. For specific details about the PQA construction, please refer to Sec. 3.4 Our PQA evaluates generated data across three dimensions: visual quality (VQ), motion quality (MQ), and composition aesthetic (CA). These individual scores aggregated into a final score, and samples exceeding a threshold are selected as our training data. We implement comprehensive grading assessment that converts model-generated scores into standardized grade scores. It employs a five-tier grading scale (A to E) with corresponding numerical ranges, as shown Tab.
[Discussion.] How to handle cases where the initial perspective is less favorable? In our pipeline, we initially treat the original image the well-composed perspective. This assumption is later refined through human preference learning during the PQA filtering (Sec. (34 and RLHF (Sec. 3.3) stages, acknowledging that the original perspective may not always be the aesthetically pleasing.
3.3 Photography Perspective Composition (PPC)
[Intuition.] Previous image composition works primarily focused on cropping approaches. In contrast, we propose video-based approach: given less favorable perspective, we generate a camera movement sequence that gradually transitions an aesthetically enhanced perspective of the scene. This video-based approach is motivated by two key observations: First, image composition represents

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Perspective Composition Recommendation
RLHF for Video Alignment
Rating this video from MQ, and parspesivv [Snsraction)]
Perspective Quality Assesmmnnt
Guidance
Dox
Scove [4.25 2.77, 7.5%)
PPC Model
Suboptinal Perspective
Optimal Perspective
Soore: (326, 3.41
Flow-DPO Loss LFD
Thans Perspective
Matching
DPO Alignment
Hammn Action
[(
PPC Model
Figure 3: The pipeline of proposed photography perspective composition (PPC).
a partial order relationship where quality assessment often relies on comparing different views of the same scene rather than isolated evaluation. Second, the transition process naturally demonstrates compositional improvements through before-and-after comparisons, making it particularly effective for both visual demonstration and educational purposes.
[Detail.] (1) Base Pipeline. As shown in Fig. 3] our pipeline takes less favorable perspective as input and generates a transformation video from the less favorable to aesthetically enhanced perspective. This process can be modeled as an image-to-video (I2V) task. 12V has seen remarkable progress, with both commercial solutions like OpenAl's Sora Runway Gen-3 (https: wwwwwaammmmmmmmmm com), and Pika 1.5 (https://pikal) art),, and open- source models like Hunyuan [17], CogVider 48 and WAN [39]. Our pipeline leverages these open-soures models generate perspective transformation videos. We utilize the last frame of the video as our final aesthetically enhanced perspective and design method to guide human actions. First, we draw guidance box (the red bbox in Fig. 3 on the enhanced perspective. Then, based on this box, along with the initial and final perspectives, we transform this box onto the original image using feature matching, creating a distorted box. As the user moves, this box gradually changes shape, approaching rectangle when reaching the aesthetically enhanced perspective. To simplify the process and accelerate computation, we only use the traditional homography transformation.
com/ sora),
(2) RLHF for Quality Enhancement. We observed that some generated perspective transformation videos, while deviating from the GT direction, still maintain high aesthetic quality. This suggests that strict GT adherence may not always yield the most aesthetically pleasing results. Therefore, we propose incorporating direct preference optimization (DPO) to align the model with human preferences. This approach encourages the exploration of aesthetically pleasing trajectories that may differ from GT, avoiding the limitation where GT-based optimization could discourage potentially superior compositional alternatives.
Our RLHF implementation primarily draws from Diffusion-DPO 30 and VideoAlign 24 Consider a fixed dataset D = {s, Uh 1. where each sample consists of prompt s and two vidcos, (higher-quality video) and en (lower-quality video), generated by reference model Pref, with human annotations indicating that ty. is preferred over (i.e., t/ > ut). The goal of RLHF is to learn conditional distribution (p(u s) that maximizes reward model r(u, s) (i.e.. PQA model proposed in Sec. 3.4), while controlling the regularization term (KL-divergence) from the reference model pref via a coefficient 3:
Eg-Da.enpe(li)) [r(u,))] BBa. [po(v) (s) Per(u |s]]-
(1)
Ps
In Rectified Flow, adopt videoalign 24 that relate the noise vector e* velocity field v*,
where 1/" represents the velocity field of either the higher-quality video (I²) or lower-quality video
(Id). Specifically, it can be proved that ||ş. - Eprea(2,, t)]l² = 10-)) +) (1pea(1/f t)]l where Epeed and Prred refer to predictions either from the model pe or the reference model Pref. Based this
5

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Table 2: Comparison of 12V models in generating perspective transformation videos.
Perspective Accuracy
Human Performance Score
Method
CMM
FVD4
PSNR
SSIM 1
IPIPS
VQ1
MQT
CA f
CogvideoX 5B 48
0.5501
303
8,2380
0.2611
0,7969
0,7073
0.7311
0.7196
Hunyuan 12V 17
0.4928
264
9,4017
0.3537
0.7915
0.7216
0.7496
0.7070
Wan2. 14B 39
0.5989
345
9,3668
0.3265
0.7808
0.7195
0.7454
0.7072
Video Quality
12V
12V
Subject Consistency
Background Consistency
Motion Smoothness
Dynamic Degree
Aesthetic Quality
Imaging
Mathod
Subject
Background
Quality
CogvidoeX 1.5 SB
0.9632
0.9639
0.9545
0.9502
0,9906
0.1347
0.5314
0.5667
Hunyuan 12V
M
0.9866
0.9878
0.9582
0.9663
0.9927
0.7851
0.5583
0.5893
Wan2.1 1 14B
39
0.9618
0.9694
0.9470
0.9435
0.9917
0.8883
0.5464
0.6299
relationship, we obtain the Flow-DPO loss (FD:
-E log (- B: ((Ik-vo(vz) vo(vf, I| -ma(l , mod'-vovvh, (ll''=r(vv,,0)' -11y' - -na(vh (((()))11)))
(-2
(2)
where 3. = B (1 t)² and the expectation is taken over samples {or, un D and the schedule
3.4 Perspective Quality Assessment (PQA) Model
[Intuition.] PQA serves dual purposes: filtering generated training data to enable automated dataset construction, and providing win-lose pairs for RLHF in PPC to align with human preferences. Due to the limitations of 3D reconstruction models, some scenes suffer from inaccurate point cloud reconstruction inpainting distortions, leading to distortion, fixedness, and blur. We propose two-stage training strategy for the PQA model that addresses the dual challenges of data volume and expertise requirements. Given that fine-tuning VLMs demands substantial training data, the first unpair-wise stage leverages large-scale, efficiently collected data to meet this requirement, as basic quality assessment does not demand expert knowledge. The subsequent pair-wise stage employs expert-annotated compositional data to refine the aesthetic capabilities of the model.
[Detail.] (1) Dateset Setting. Stage @: Unpaired Videos. This stage focuses on distinguishing video quality levels. We collected approximately 5K perspective transformation videos generated by 3D reconstruction models, with expert annotators identifying roughly 1.5K high-quality and 3.5K low-quality samples. To expand the dataset, we randomly paired each high-quality video with 10 low-quality ones, creating 15K unpaired dataset. Stage 2: Paired Videos. This stage focuses composition aesthetic recognition through paired comparison learning. For each initial perspective, we generate three video clips using separately trained VideoX 1.5, WAN 2.1, and the original GT data. These clips are paired with each other, where "paired" indicates videos sharing identical input views. Expert annotators evaluate these pairs across three dimensions: visual quality (VQ), motion quality (MQ), and composition aesthetic (CA). For each dimension, annotators choose between options (A wins/Ties/B wins). Notably, the CA metric assesses the compositional improvement throughout the video transformation rather than static frame quality. Detailed annotation guidelines are provided in Appendix A
(2) Model Setting, Our model primarily follows the architecture of VideoAlign 24 We employ Qwen 2- VL extends the traditional Bradley-Terry framework[2) To better handle multi-dimensional evaluation, separate special tokens for context-agnostic (visual quality, motion quality) and composition-awaal (composition aesthetic) attributes, leveraging the causal attention mechanism to achieve effective feature decoupling. The model predicts rewards for each dimension through shared linear projection head applied to the corresponding token representations from the final layer.
base model, utilizing the Gradley-Terry model with ties loss (BTT) 31, which
4 Experiments
In this section, we evaluate our approach through two main components: photography perspective composition (PPC) and perspective quality assessment (PQA).

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Figure 5: PPC performance in single-subject scenarios.
4.1 Investigation for Photography Perspective Composition (PPC)
Implementation. To comprehensively validate approach, experimented with three state- of-the-art video generation models: Cog Vogee 1.5 [48]. HunYuan [7]. and 1 [3]. The training parameters follow the settings from the original repository.
Metric Design. Since there is no prior work on photography com- position using perspective trans- formation, we need to define met- rics. We evaluate the generated videos from three parts: perspec- tive accuracy, video quality, and human performance score. For video quality assessment, we pri- marily adopt the evaluation met-
Sukject Consbitenyy
Subject
Rackgrouni Consistency
12v Bachgroand
Rackground Consistency
12v Bachground
Smoothipsss
Subject
Smosthinese
Subject
Dynaciic
Imaging Aasthwtic Quality
Dynaseli
trsiging
Aasthwtic Quality
rics from VBench2.0 [15] I2V benchmarks, which include I2V subject, 12V background subject consistency, background consis- tency, motion smoothness, dy- namic degree, aesthetic quality, and image quality. For perspec- tive accuracy evaluation, we em- ployed both video distance and image similarity metrics. The video distance was measured using camera motion matching (CMM) and Fréchet Video Distance (FVD) [37] while image similarity was assessed using CLIP-F 29 PSNR, SSIM, and LPIPS. Notably, we modified the camera motion section of the original vbench [5] changing it to camera motion matching. As we found that the camera motion detection model in vbench did not work well, especially for small angle changes. Instead, we now pass both the pre- diction and ground truth through the camera motion detection and then calculate the accuracy by matching these two detection results." As for the human performance score, its purpose is to simulate human preferences in scoring. This is conducted through PQA. which established in Sec. 3.4 and includes three components: visual quality (VQ), motion quality (MQ), and composition aesthetic (CA).
20%
40%
80%
107%
10"
20
30
Mik
vo
MG
100%
100%
100%
60%
80%
40%
40%
40%
20"
20'
20%
20%
20%
201
1."
10
(a) Training Data Ratios
(b) Training Data Angles
Figure 4: Quality and human performance results for PPC.
Main Results. /Quantitative Results) As shown in Tab. we demonstrate the performance of three 12V models in generating PPC videos. [Qualitative Results] We demonstrate the versatility of our approach across three representative scenarios. The first scenario addresses single subjects (e.g., human figures and animals), as shown in Fig. 5 where our PPC model enhances compositional harmony by seamlessly integrating subjects with their surroundings. In the second scenario (Fig. 7 tackle multi-subject scenes, demonstrating how PPC achieves balanced spatial arrangements
7

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Table 3: Quantitative Result for PQA (Tab. (a) and Tab.(b)) and PPC (Tab. (c) and Tab. (d)).
(a) The number of pairs.
(b) Different steps.
(c) The data ratios.
(d)
The data angles.
Accuracy
Accanes
Ratio
CMM 1
FVD
CMM
IVD J
Puics
CA
Steps
VQ2
MQ
CA
20%
0.5014
10"
0.4413
397
100
0.8019
0,7772
B.6515
08008
0.5957
0.0055
0,7812
0.0655
0.5881
0.7894
08002
08800
BTT Siggle
Rey Sinple
WTT Tho
Bag
0.2814
0,5509
0,8099
0.4986
0,5117
038085
0.41
0300
04913
100%
80%
40%
0.5673
0.5244
0.5989
345
362
Mix-up
20"
30"
0.3983
0.5969
0.5587
444
337
to elevate overall visual aesthetics. The third scenario explores landscape photography (Fig. B) addressing two common challenges faced by novice photographers: balance and horizontal alignment. Our model effectively optimizes these scenes, particularly enhancing symmetrical compositions. Beyond these scenarios, we discovered the applicability of PPC to UAV photography. As illustrated in Fig. 9 our model successfully identifies aesthetically enhanced views from drone-like perspectives, generating camera movements that adhere to compositional principles while maintaining aesthetic appeal.
PPC Angles. To maintain high consistency in our recommendation system, we limit perspective transformations short angles, ensuring our suggestions are reliably based on the actual visible content in the current scene. We also investigated the impact of PPC angles on performance using three distinct rotation degrees (10%, 20", and 309) and a balanced mixed dataset incorporating all angles. As shown in Tab [3 and Fig.4p. we observe that while performance remains stable at 10", both quality and accuracy metries deteriorate significantly when the dataset rotation angles reach 30", We attribute this degradation to the substantial visual disparity between the original and transformed views at larger angles, which makes challenging for the model to learn generalized aesthetic perspectives.
Original Perspective
wio RLHF
with RLHF
The Consistency of PPC. Fig. 6p demonstrates our model's consistency in PPC. When presented with different less favorable views of the same scene, model generates consistent aesthetically enhanced perspectives, maintaining coherence across different inputs.
(a) Qualitative comparison of RLHF
The Effection of RLHF in Video Quality Enhance- ment. We evaluate the performance after incorporat- ing RLHF (Sec. (.3). Fig. G and Tab. demonstrate the effectiveness of incorporating RLHF. The results show that RLHF leads to more stable subject gener- ation.
(b) PPC maintains perspective consistency
4.2 Investigation
Figure 6: Qualitative comparison of RLHF
for Perspective Quality Assessment (PQA)
and the perspective consistency of PPC.
Implementation and Evaluation Metric. We utilize Qwen2- VL-2B 40 as the backbone for PQA and train it with BTT loss. To fine-tune the model, LoRA [3] is applied to update all linear layers in the language model, while the vision encoder's 's parameters are fully optimized. The training process conducted with batch of 32 and a learning rate of 2 x 10-6, with the model trained over two epochs. This setup requires approximately 50 NVIDIA H20 GPU hours. Several observations were made during training. We sample videos at fps, with a resolution of 448 x 448 pixels during the training process. Following previous works (4], we adopt accuracy the metric for each dimension.
Main Results. /Quantitative Result] To investigate the impact of training data volume in stage 1, we conducted experiments with varying numbers of video pairs, as detailed in Tab 3a, Our results reveal that while perfor- mance generally improves with increasing sample size, it plateaus at approximately 100 samples, suggesting that this quantity provides sufficient diversity for model performance. /Qualitative Results] Fig. I0 demonstrates the basic effects of PQA. As shown in
Table 4: The effect of RLHF in PPC.
CMM
FVD
VQ
MQ
CA
w/o RLHF 0.4928 264.7672 0.7216 0.7496 0.7070 with RLHI 0,5014 270.2212 0,7477 0,7774 0.7342
8

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Figure 7: PPC performance in multiple-subject scenarios. scenaioss
Figure 8: PPC performance in wide landscape views and asymmetric scenarios.
Fig. 4a, while there is notable improvement in composition, the video quality remains low. Fig. 4p exhibits acceptable levels of both VQ and MQ, but shows minimal compositional differences, result- ing lower CA score. Notably, PQA assigns lower ratings static or too intense, as illustrated in the last two examples.
where the perspective remains
evaluated the effectiveness of the two-step approach under both regression and BTT loss 24 functions. The single-step approach, lacking sufficient data enhance baseline performance, demonstrates significantly lower overall performance compared to the two-step way.
Effect of Two Steps. As shown in Tab
Conclusion and Limitation
Conclusion. this work, we addressed the limitations of previous photography composition methods by introducing photography perspective composition (PPC), a novel paradigm that extends beyond 2D cropping to achieve 3D recomposition. Our approach is inspired by real-world street photography practices where photographers use perspective adjustment establish better relative relationships between subjects. To overcome the challenges of implementing PPC, particularly the lack of suitable datasets and unclear assessment criteria, we made three significant contributions. We developed framework for automatically constructing PPC dataset from expert photographs, created a system for generating perspective transformation videos that guide users from less favorable aesthetically enhanced views, and introduced the perspective quality assessment (PQA) model that evaluates both video quality and compositional aesthetics. We hope this work opens up new possibilities in computational photography and inspires further research in perspective- composition.

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RWRN 10
0MR8 1,00
Figure 9; PPC performance in UAV-like scenarios.
VQ: 0.009; MQ; 5.875; CA: 3,578
VQ: 5.531; MQ: 7.563; CA 110016
VQ: -7.281; MQ: -75000; CA: 99063
VQ: -6.65; MQ: 5,999 CA: -6750
Figure 10: Quantitative Results of PQA.
Limitation and Future Work.
(1) Video Duration. Current PPC constrained by the limitations of existing video models, particularly in terms of duration. In the latest phase, we have also discovered AR-based video generation models that can generate infinite streaming videos. We believe this work can provide broader insights and directions for PPC.
(2) Video Quality. Since our training data is generated by 3D reconstruction models, the performance of PPC is inherently limited by current reconstruction capabilities. A crucial direction for future improvement lies in exploring superior methods for generating perspective transformation videos, such as utilizing the Unreal Engine for video generation.
(3) Data Scaling Behavior. Despite the strong scene diversity provided by numerous expert pho- tography images,
observed that model outputs become unstable as the training data volume in- creases. As shown in Tab. 3c Fig. 4a, and Fig. II while both accuracy and quality initially improve with increasing data volume. performance deterio- rates when the training set size grows further. We hypothesize that this challenge lies in maintain- ing the desired model behavior to ensure proper perspective rather than deviating into unintended random behaviors. This phenomenon bears similar- ities what was described IC-Light [52). and we plan to incorporate their training methodology to investigate whether it can enhance training stability in our future work.
Figure 11: Diverse data presents instability.
10

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Please refer to our LLM policy (https: /ttt:::///euurppe htt.rrippe.cc/ooooeenneeee/////s /202/ LLM) for what should or should not described.
21

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Appendix
A
Prompt Instructions
For VQ and MQ, our instructions primarily follow those of VidcoAlign (24). We specifically designed the CA instructions shown below:
Input Template (CA part)
** "CCompostioon *estheti
Evaluate the evolution and sophistication compositional techniques throughout the video, drawing inspiration from Magnum Photos' aesthetic principles. Consider the following sub-dimensions:
**I *aLyerrn Complexity** Assess how the video utilizes multiple planes and creates depth through foreground, middle ground, and background interactions. Consider if these spatial relationships become more sophisticated over time.
**Gometric Harmony**; Evaluate the use of strong geometric elements, lines, and shapes that create dynamic tension and visual interest, similar Alex Webb's approach complex frame organization.
Color Relationships: Consider how color blocks and contrast are used compositionally to create visual weight and guide viewer attention through the frame.
**FFrme Utilization Evaluate how effectively the entire frame used, including edges and corners, and how secondary elements support the main subject.
** Visual Rhythm** Consider the pattern and repetition of elements, and how they create compositional flow and movement within the frame.
** JJxtapposition Development** Assess how the video develops and maintains meaningful visual relationships between different elements in the frame.
Please provide the ratings of Composition Aesthetic: <ICA reward
END
Instruction Source. The compositional principles our framework are derived from seminal works in photography theory and practice. These include the layering complexity theory from Alex Webb's 's "The Suffering of Light" and Sam Abell's three-layer composition approach; geometric harmony principles from Henri Cartier-Bresson's 's "The Decisive Moment": color relationship theories from Steve McCurry and Ersst Haas; frame utilization techniques from Robert Frank's "The Americans"; and visual rhythm concepts from Paul Strand and Minor White. These principles, extensively documented in the works of Magnum Photos photographers, form the theoretical foundation for our composition assessment criteria.
Table Key points summary outlined in annotation guidelines for CA evaluation dimension.
Evaluation Dimension
Key Points Summary
Considering the following dimensions in the compositional design of the video:
Compositional Reasonableness: The composition should be objectively reasonable and well-balanced.
Compositional Clarity: The arrangement of elements should clear and visually organized.
Composition Aesthetic
Compositional Detail: The level of sophistication in the arrangement and relationship between elements.
Compositional Creativity: The composition should be aesthetically pleasing and show creative arrangement.
Compositional Safety: The composition should not create visual tension uncomfortable viewing experience.
22


Reviews:
[{"review_id": "xjzYAyJvge", "reviewer": "Reviewer_5rXU", "summary": "This 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.", "questions": "see above", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "I remain positive on this paper.\n\nThe points that convince me: \n- good novel use of video generators to create artifical, bad data for training the inverse task \n- interesting problem statement, potentially with larger impoact to the field \n- well grounded in technical literature, using eg RLHF, etc", "rebuttal": "We sincerely thank Reviewer 5rXU for recognizing the novelty of our work, particularly appreciating our innovative approach to photography composition through video models that transcend traditional 2D cropping methods. Below, we provide detailed responses to each concern. The \"W\" represents weakness.\n\n> W1. About the PQA method.\n\nWe appreciate the reviewer for this point. We agree that as video generation models continue to advance, the reliance on PQA for quality assessment will naturally decrease. The improved capabilities of future video generation models will likely reduce the need for explicit quality filtering. Our technique will benefit from these continuous advancements in video generation models, making this research area promising for long-term development.\n\nHowever, we think that the evaluation model like PQA still has its unique value **beyond video generation models**.\nWe are actively developing our next-generation system using **Unreal Engine 5-based** data collection pipelines to generate higher fidelity training scenes. In this context, **the evaluation model becomes particularly valuable for automated data collection workflows**: it can efficiently evaluate whether a generated scene is worth collecting by providing composition quality scores, enabling large-scale automated curation of high-quality training data without manual intervention. This automated scene assessment capability will be instrumental in scaling up our data collection efforts. \n\n> W2. About the model hallucination and detail changes affecting sensitive scenarios like portrait photography.\n\nWe thank the reviewer for this concern about consistency issues. For sensitive scenarios like portrait photography, \ndetail inconsistencies and hallucinations are inevitable.\nThese issues primarily occur in background regions, especially when the model needs to generate background content not present in the original image. Actually, it is an inherent challenge for generative models.\n\nHowever, we find that these background detail distortions do not affect our understanding of the perspective transformation guidance provided by the model. This aligns with our core objective of providing perspective transformation guidance through videos.\n\nAdditionally, to minimize the occurrence and impact of these consistency problems, we also make some efforts:\n\n**(1) Angle Limitation.** During data generation, we limit the maximum angle difference (20°) between the first and last frames of training videos. In our initial experiments without this limitation, the model often exhibited severe hallucinations by generating completely new scenes. By constraining the angle difference, we ensure the model learns to perform gradual perspective transformations rather than generating entirely different scenes, which significantly reduces hallucination issues. We found this angle limitation to be an effective solution for maintaining visual consistency while still allowing meaningful perspective changes.\n\n**(2) RLHF.** Even after implementing angle limitation, we still observe some videos with larger transformations that significantly affect the main subjects. Upon analyzing these problematic cases, we discover an interesting phenomenon: while these videos exhibit notable distortions, their perspective changes actually aligned well with human aesthetic preferences, but they deviated from the ground truth (GT) transformation directions. This led us to reconsider **whether strict GT adherence was necessary—perhaps alignment with human preferences would be sufficient**.\nTherefore, we introduce a two-stage RLHF training to relax the rigid GT constraints and allow the model to learn from human preference feedback rather than forcing exact GT compliance. This approach addresses the fundamental trade-off between GT fidelity and natural aesthetic transformations that humans find appealing.\nThe effectiveness of this RLHF approach is demonstrated in Figure 6(a) in the paper: without RLHF, the cat's head directly deviates from its original position during perspective transformation, violating natural perspective relationships. After incorporating RLHF, the cat's head maintains proper perspective consistency, following realistic spatial transformations that align with human expectations.\n\n**(3) Future Improvements with Unreal Engine 5.** As mentioned above, we are developing our next-generation system using Unreal Engine 5-based data generation pipelines to further enhance scene consistency. This game engine approach provides significantly more controlled environments for generating training data. With perfect lighting control, we can eliminate the lighting inconsistencies that commonly occur in real-world 3D reconstruction. The precise camera tracking capabilities allow for exact trajectory control, ensuring consistent perspective transformations. Additionally, the engine maintains perfect structural consistency across all frames while avoiding the reconstruction artifacts and hallucinations that are inherent in current image-to-video approaches. This controlled data generation environment will be instrumental in reducing consistency issues.\n\n> W3. About the overshooting.\n\nWe thank the reviewer for the careful review. Precise control over an exact \"best perspective\" is indeed challenging due to two main factors: (1) the subjective nature of perspective aesthetics itself, as different photographers may have varying preferences for what constitutes the \"best\" perspective for a given scene. (2) the inherent characteristics of video generation models.\n\nWe initially explore a more straightforward angle prediction approach, designing classification models for four-directional angle predictions (5°, 10°, 15° increments). This classification-based method shows promising results and improved accuracy with increased data.\n\n**Angle Prediction Classification Results.**\n\n| Direction | Angle | 10K Data | 20K Data | 30K Data |\n|-----------|-------|----------|----------|----------|\n| Horizontal Left | 5° | 78.2% | 82.5% | 85.1% |\n| Horizontal Left | 10° | 74.6% | 79.3% | 82.7% |\n| Horizontal Left | 15° | 71.3% | 76.8% | 80.4% |\n| Horizontal Right | 5° | 77.9% | 81.8% | 84.6% |\n| Horizontal Right | 10° | 74.1% | 78.9% | 82.3% |\n| Horizontal Right | 15° | 70.8% | 76.2% | 79.9% |\n| Vertical Up | 5° | 76.4% | 80.7% | 83.8% |\n| Vertical Up | 10° | 72.8% | 77.6% | 81.2% |\n| Vertical Up | 15° | 69.5% | 74.9% | 78.6% |\n| Vertical Down | 5° | 75.8% | 80.1% | 83.3% |\n| Vertical Down | 10° | 72.2% | 77.1% | 80.8% |\n| Vertical Down | 15° | 68.9% | 74.3% | 78.1% |\n\nHowever, we find that simply predicting discrete angles do not align with our original intention. We aim to provide intuitive, educational guidance that demonstrates the transformation process, which led us to persist in adopting the video generation approach for creating instructional perspective transformation videos.\n\n**About the writing.**\n\n>1. Usage of the word \"optimal\" in subjective contexts.\n\nWe appreciate the feedback on writing clarity and will replace \"optimal\" with \"improved\" or \"enhanced perspective\" throughout the paper to avoid subjective claims.\n\n>2. Logical issues with sentences starting with \"While\".\n\nWe will revise the \"While\" problem and further polish our paper.\n\nFor L21, the revised version is: \"Master photographers, such as those in Magnum Photos, require professional knowledge and extensive training, making quality photography expensive and challenging for ordinary people.\"\n\nFor L25, the revised version is: \"Numerous approaches have been developed for image cropping, including saliency-based methods, learning-based techniques, and reinforcement learning strategies.\"\n\n>3. Missing NICER reference in the related work section.\n\nWe will add the NICER reference (Fischer et al.) to the related work section on \"Evaluation with Human Performance\"."}, {"review_id": "C9hXTY6W5M", "reviewer": "Reviewer_4nUR", "summary": "The 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.", "questions": "- 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.", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "#### 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.", "was_revised": true, "final_justification": "Most of my concerns have been resolved and I have decided to increase the score to 4", "rebuttal": "We sincerely thank the reviewer's feedback and constructive suggestions. Here are our responses to the reviewer's comments. The \"W\" represents weakness, \"Q\" represents question, and numbers in parentheses (e.g., Q2(1)) represent sub-questions of that question.\n\n> Q1 & W2. About the data ratio influence.\n\nWe are continuing to conduct experiments and have found that the core issue may lie in the significant gap between our perspective transformation data and the original pretraining data of video generation models. Most of the original pretraining data consists of movie clips and internet videos, which differ substantially from our perspective transformation videos in terms of motion patterns and visual characteristics.\n\nSince the original pretraining data of video generation models is not publicly available, we use VBench's (CVPR24) test videos as a proxy to approximate the original distribution. VBench provides a comprehensive video quality benchmark with diverse video samples. We compare our perspective transformation data against this VBench distribution to quantify the distributional gap:\n\n| Dataset Ratio | KL-Divergence ↓ | JS-Divergence ↓ | \n|-------|----|------|\n| 20% | 0.42 | 0.28 | \n| 40% | 0.58 | 0.34 | \n| 80% | 0.84 | 0.47 | \n| 100%| 1.12 | 0.61 | \n\nAs dataset diversity increases, all distribution divergence metrics worsen significantly. This distributional shift forces the model to deviate from its learned priors.\n\n>Q2(1) & W1 & W4. User usability assessment.\n\nWe conduct a preliminary user survey to validate our method's practical value. Due to time constraints, our survey scale is not very large but focus on gathering meaningful feedback about the system's usability and effectiveness.\n\n**(1) Questionnaire Design.**\n\nWe design and distribute an online questionnaire, and we receive 57 users' feedback. The design of the questionnaire is as follows:\n\n**A. Background Assessment**\n```\nA1. Photography Experience:\n    □ Beginner (<6 months)         □ Novice (6-24 months)\n    □ Intermediate (2-5 years)     □ Advanced (>5 years)\n\nA2. Primary Equipment:\n    □ Smartphone only (iPhone, Samsung, Vivo, Google Pixel)\n    □ Point-and-shoot camera (Ricoh GR III, Fujifilm X100V)\n    □ DSLR/Mirrorless (Canon R5, Sony A7IV, Fujifilm X-T5)\n    □ Professional gear (Hasselblad, Leica)\n\n```\n\n**B. PPC System Evaluation**\n\n```\nWe set up 8 diverse scenario cases to evaluate the system's performance across different photography contexts. For each scenario, users evaluate the guidance videos generated by PPC.\n\n*(7-point Scale: 1=Strongly Disagree, 4=Neutral, 7=Strongly Agree)*\n\nB1. The final perspective significantly improves upon the original photo\nB2. The transformation sequence is intuitive and easy to follow\nB3. The suggested compositions align with my personal aesthetic preferences\n```\n\n**C. Comparative Assessment with Crop-Based Methods**\n```\nWe prepared 12 test cases covering diverse photography scenarios. For each case, we applied two SOTA cropping methods (Gencrop (AAAI24) and Cropper (CVPR25)) and selected the highest-confidence crop result.\n\nC1. Overall composition improvement compared to the best cropping result:\n    Rate PPC performance: 1 (Much worse), 2 (Worse), 3 (Similar), 4 (Better), 5 (Much better)\n\nC2. Content preservation compared to cropping methods:\n    Rate PPC performance: 1 (Much worse), 2 (Worse), 3 (Similar), 4 (Better), 5 (Much better)\n```\n\n**(2) Results.**\n\n**A. Participant Profile Analysis:**\nOur participants had diverse photography experience: 34% beginners, 44% novice, 19% intermediate, and 3% advanced. For equipment, 41% used smartphones, 25% point-and-shoot cameras, 31% DSLR/mirrorless, and 3% professional gear.\n\n**B. System Evaluation Results (7-point Scale)**\n\n|Evaluation Dimension|Mean|High Satisfaction (6-7)|Neutral/Low (1-4)|\n|----------|-------|-----------|---------|\n| **B1. Final Result Improvement** | 5.6 | 75% | 16% |\n| **B2. Transformation Clarity** | 5.4 | 69% | 19% |\n| **B3. Aesthetic Alignment** | 5.0 | 59% | 28% |\n\n**C. Comparative Assessment Results**\n\n| Evaluation Dimension | Mean Score | Rating 4-5 (Better/Much Better) | Rating 1-2 (Worse/Much Worse) |\n|---|----|----|----|\n|**C1. Overall Improvement**|4.2| 78% | 6% |\n|**C2. Content Preservation**| 4.6 | 88% | 3% |\n\n> Q2(2). First vs last frame.\n\nIn our user study described above, we conduct first vs. last frame comparison. Specifically, item **B1 \"The final perspective significantly improves upon the original photo\"** received positive feedback: mean score of **5.6/7.0**, with **75% of participants** rating high satisfaction (6-7 points) regarding the aesthetic improvement from initial to final frames.\n\nTo further validate these findings on a larger scale, we also conduct aesthetic assessment using VILA on our test set. Since this evaluation covers multiple diverse videos rather than individual videos, we employed **aesthetic improvement ratio** as our primary metric.\n\n| Improvement Category | Score Increase | Percentage |\n|------|------|------|\n| **Significant** | >0.1 | 26.5% |\n| **Moderate** | 0.06-0.08 | 34.4% |\n| **Minor** | 0.02-0.06 | 26.1% |\n| **No Change** | -0.02-0.02 | 9.5% |\n| **Decline** | < -0.02 | 3.5% |\n\nThe results demonstrate consistent aesthetic improvements across our PPC. We plan to conduct more extensive experiments with a larger and more diverse user base for further validation.\n\n>Q3. About the low-quality inputs.\n\nWe thank the reviewer for their comments and have conducted additional experiments accordingly.\nWe used Q-Align (ICML24), a commonly used Image Quality Assessment model, to identify and categorize low-quality images. \n\nQ-Align provides quality scores on a 0-5 scale. Our experiment images consist of two main categories: high-quality and low-quality images. The high-quality subset contains 300 images with Q-Align scores of 3 or higher. The low-quality subset comprises 300 images with Q-Align scores of 2.0 or lower. \n\n| Quality Level | CMM ↑ | FVD ↓ | VQ ↑ | MQ ↑ | CA ↑ |\n|---------------|-------|-------|------|------|------|\n| **High-Quality** | 0.61 | 330 | 0.750 | 0.748 | 0.711 |\n| **Low-Quality** | 0.58 | 341 | 0.715 | 0.720 | 0.702 |\n\nThese results show that while there is some performance degradation on low-quality inputs, PPC still maintains acceptable performance levels compared to all inputs. \n\n>Q4. About the implementation.\n\nTo provide users with more intuitive and clear guidance, we adopt the video generation approach to implement our PPC, which is a novel direction that has not been explored before in photography composition guidance. However, typical video generation models can take 5 minutes to generate a few seconds of video, mainly due to model size, diffusion steps, and frame count requirements. \n\nTo address this inference time issue, we make several optimization attempts. Most importantly, we observed that our perspective transformation task does not require as many frames, diffusion steps, or high resolution as conventional video generation. By reducing the frame count to 9 frames, using only 10 diffusion steps (typically 50), and generating at a lower resolution, we achieved over 60x speedup while maintaining effectiveness for our specific use case. For the CogVideoX 1.5 5B model, the inference time is 5.1 seconds with 10 GB GPU memory usage under BF16 precision.\n\nWith further optimization for deployment engineering and the rapid advancement of video generation models, PPC will become faster and more suitable for everyday photography use.\n\n>Q5. Analysis with existing cropping-based methods.\n\nWe conduct comparisons from two aspects: a user study and VILA aesthetic evaluation.\nOur comprehensive user study (Q2(1) Section C above) demonstrates PPC's superiority over cropping methods across multiple scenarios.\nTo provide objective validation beyond subjective user preferences, we employ VILA to evaluate **800 image pairs** comparing PPC perspective transformations (last frame) against SOTA cropping results from Gencrop (AAAI24) and Cropper (CVPR25).\nVILA evaluated both PPC-transformed images and crop-enhanced images on the same 0-1 aesthetic quality scale. The percentage improvements in aesthetic scores **compared to original images** were calculated into different ranges:\n\n| Method | Mean Score vs. Original | 0-2% | 2-4% | 4-6% | 6-8% | 8-10% | >10% | Decline |\n|--------|------------------------|------|------|------|------|-------|------|---------|\n| **Gencrop** | +0.05 | 44% | 25% | 14% | 7% | 3% | 1% | 6% |\n| **Cropper** | +0.12 | 30% | 29% | 21% | 9% | 4% | 2% | 5% |\n| **PPC (Ours)** | +0.18 | 22% | 26% | 22% | 16% | 8% | 3% | 3% |\n\nThis result shows that perspective-based recomposition offers additional advantages in addressing challenges compared to cropping-based approaches.\n\n>W3. CA metric subjectivity and annotator bias.\n\nWe have conducted an inter-annotator reliability analysis. Our PQA model training employed 8 expert annotators. For the dataset, videos are independently annotated by 3 annotators using pairwise comparison across three dimensions. \n\nTo quantitatively evaluate the consistency between annotators, we calculated Fleiss' Kappa coefficient, which measures the degree of agreement among multiple raters. \n\n| Metric | Score | Interpretation |\n|---|---|---|\n| VQ | κ = 0.78 | Substantial |\n| MQ | κ = 0.70 | Moderate|\n| CA | κ = 0.74 | Substantial |\n| Overall Multi-Annotator | κ = 0.71 | Substantial |\n\nWe can observe that our annotations demonstrate substantial consistency across three metrics."}, {"review_id": "ob2QuiqKKs", "reviewer": "Reviewer_rmgK", "summary": "The 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.", "questions": "In 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?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\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).", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns, though I am still concerned about some artifacts (e.g., structure distortion and loss of details) shown in some of the current visual results. The authors have discussed two strategies to mitigate the artifacts in the rebuttal and shown their effectiveness with added quantitative results. However, it is difficult to tell if these strategies really improve the visual quality of results to a reasonable level without visual comparison. Therefore, I decide to keep my initial rating.", "rebuttal": "We sincerely thank the reviewer for the positive feedback and constructive suggestions on our paper. Here are our responses to the reviewer's comments. We use different letters to represent different types of comments, where \"W\" represents weakness and \"Q\" represents question.\n\n>Q1. About the camera motion trajectory generation.\n\nOur camera trajectories are not purely random but incorporate systematic design principles. We have explored and experimented with multiple trajectory generation strategies across different complexity levels.\n\n**(1) Basic Single-Direction Movements.**\nWe first established fundamental directional movements using ViewCrafter's parameter system. Our basic movements include four primary directions: left and right movements involve horizontal angle adjustments while maintaining vertical position, creating lateral camera motion for reframing subjects. Up and down movements adjust the vertical angle while keeping horizontal position constant, allowing for elevation changes that can improve horizon placement or alter subject prominence within the frame.\n\n**(2) Compound Dual-Direction Movements.**\nBuilding on basic movements, we explore diagonal combinations that provide more sophisticated compositional adjustments. These compound movements simultaneously adjust both horizontal and vertical angles to create diagonal camera trajectories. Upper-left and upper-right movements combine upward motion with lateral adjustments, particularly useful for horizon placement optimization and diagonal composition rebalancing. Lower-left and lower-right movements blend downward motion with horizontal shifts, helping to improve subject-background relationships and achieve perspective correction in architectural or landscape photography.\n\n**(3) Complex Multi-Phase Movements.**\nWe also experiment with complex motion patterns for advanced compositional scenarios. Our experiments systematically explore different complexity levels of camera movements. (1) We employ 3-step patterns (40% of cases) that sequentially combine horizontal, vertical, and diagonal movements to address fundamental compositional imbalances through systematic perspective correction. (2) We utilize 4-step patterns (30% of cases) that enhance the basic sequences by adding zoom adjustments to the 3-step movements, enabling simultaneous optimization of both framing and perspective. (3) We also implement 5-step patterns (30% of cases) that were built upon the 4-step sequences by randomly adding one additional movement type (horizontal, vertical, diagonal, or zoom), providing enhanced flexibility for complex compositional refinements.\n\nWe evaluate different trajectory strategies using metrics from our experiments:\n\n| Trajectory Strategy | CMM Score ↑ | FVD ↓ | VQ ↑ | MQ ↑ | CA ↑ |\n|---------------------|-------------|-------|------|------|------|\n| **Basic Directional** | 0.63 | 318 | 0.75 | 0.78 | 0.74 |\n| **Compound Dual** | 0.61 | 332 | 0.73 | 0.76 | 0.73 |\n| **Complex Multi-Phase** | 0.58 | 333 | 0.70 | 0.74 | 0.71 |\n\nHowever, for amateur users, **overly complex camera motion trajectories may be difficult to understand and reproduce**. Therefore, we adopt a simplified hybrid approach combining three types of movements.\nWe primarily utilize single-step movements (40% of cases), two-step movements (40% of cases), and three-step movement sequences (20% of cases). This distribution ensures that the guidance remains straightforward while still accommodating more sophisticated compositional needs when required.\n\n>W1. Comparison to cropping-based methods.\n\nWe conduct comparisons from two aspects: user study and VILA aesthetic evaluation. \n\nWe conduct a user study with 57 amateur users through questionnaire. For other detailed designs, please refer to our response to Reviewer 4nUR's Q2(1). \n\n(1) We prepared 12 test cases covering diverse photography contexts (portrait, landscape, architecture, multi-subject scenes). For each scenario, we applied two SOTA cropping methods (Gencrop (AAAI24) and Cropper (CVPR25)) and selected the highest-confidence crop result as the baseline comparison.\n```\nC1. Overall composition improvement compared to the best cropping result:\n    Rate PPC performance: 1 (Much worse), 2 (Worse), 3 (Similar), 4 (Better), 5 (Much better)\n\nC2. Content preservation compared to cropping methods:\n    Rate PPC performance: 1 (Much worse), 2 (Worse), 3 (Similar), 4 (Better), 5 (Much better)\n```\nHere are the results:\n\n| Evaluation Dimension | Mean | High Satisfaction (4-5) |\n|----------------------|-----------|------------------------|\n| **C1. Overall Improvement** | 4.2 | 78% |\n| **C2. Content Preservation** | 4.6 | 88% |\n\nThe results suggest that users generally prefer our PPC method over cropping approaches. In particular, users appreciated that PPC preserves more image content (88% high satisfaction, mean 4.6/5) compared to cropping, which removes parts of the image. The overall improvement scores (78% high satisfaction, mean 4.2/5) indicate that perspective-based recomposition could be a promising alternative to traditional cropping.\n\n\n(2) To provide objective validation beyond subjective user preferences, we also employ VILA to evaluate **800 image pairs** comparing PPC perspective transformations (last frame) against best-performing cropping results from Gencrop and Cropper.\nVILA evaluated both PPC-transformed images and crop-enhanced images on the same 0-1 aesthetic quality scale. The percentage improvements in aesthetic scores **compared to original images** are categorized into different ranges:\n\n| Method | Mean Score vs. Original | 0-2% | 2-4% | 4-6% | 6-8% | 8-10% | >10% | Decline |\n|--------|------------------------|------|------|------|------|-------|------|---------|\n| **Gencrop** | +0.05 | 44% | 25% | 14% | 7% | 3% | 1% | 6% |\n| **Cropper** | +0.12 | 30% | 29% | 21% | 9% | 4% | 2% | 5% |\n| **PPC (Ours)** | +0.18 | 22% | 26% | 22% | 16% | 8% | 3% | 3% |\n\nThese results demonstrate that our perspective-based recomposition method provides superior performance compared to traditional cropping-based approaches.\n\n\n>W2. About using synthesis-based approach.\n\nThanks for the reviewer's concern. Synthesis-based approaches do suffer from inherent limitations, including synthesis artifacts and input-output content inconsistency.\n\nDespite these limitations, our core motivation addresses scenarios where cropping-based methods cannot provide adequate solutions. In many compositional challenges, cropping is inherently insufficient because it operates solely within the 2D image plane and cannot address spatial relationship issues or perspective distortions.\n\nTo mitigate these consistency issues, we have implemented several technical strategies:\n\n(1) We restrict perspective transformations to controlled angles (20°) to maintain visual consistency and reduce structural alterations. This constraint significantly minimizes the likelihood of severe hallucinations while preserving meaningful perspective guidance.\n\n(2) Our RLHF approach aligns generated outputs with human aesthetic preferences, reducing artifacts through preference-based optimization rather than forcing strict ground truth adherence.\n\n(3) We are developing Unreal Engine 5-based data generation pipelines for more controlled, higher-quality training data that will fundamentally reduce synthesis artifacts.\n\n>W3. Comparison with the direct image-to-image approach.\n\nWe sincerely appreciate this thoughtful suggestion. We initially explored image-to-image (I2I) approaches, but encountered two fundamental challenges that led us to abandon this direction:\n\n**(1) Guidance Limitation.** I2I approaches often generate images with excessive angular changes, producing completely different perspectives that users find difficult to interpret for practical guidance. When users are presented with only the initial and final images, they struggle to understand the specific camera movements or positioning adjustments needed to achieve the transformation in real-world scenarios.\n\n**(2) Training Challenges.** We initially experimented with I2I approaches but encountered problems when applied to perspective transformations. During training, the models frequently converged to simply reproducing the original input image, failing to learn meaningful perspective adjustments. This convergence issue makes it difficult to achieve reliable perspective transformation outputs. Additionally, even when the models do learn to transform perspectives, they often generate inconsistent results with severe artifacts and content distortions.\n\nOur core idea focuses on providing **instructional guidance rather than just a final result**. Users need to understand how to achieve the transformation in real scenarios, which requires demonstrating the step-by-step perspective adjustment process. The video-based approach naturally provides this educational component by showing the gradual transformation sequence, enabling users to internalize the movement patterns and apply them in their own photography practice. As video generation models continue advancing, this approach becomes increasingly viable and effective."}, {"review_id": "UpseaW1gvr", "reviewer": "Reviewer_CvRK", "summary": "The 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.", "questions": "1. 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?", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths\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.", "was_revised": true, "final_justification": "The authors have sufficiently addressed most of my concerns during the rebuttal phase. I am therefore raising my score to Borderline Accept.", "rebuttal": "We sincerely thank the reviewer for the careful review and constructive comments. Below, we have provided a point-by-point response. The \"Q\" stands for question and the \"W\" stands for weakness. Numbers in parentheses (e.g., W1(1)) represent sub-questions of that question.\n\n>Q1 & W1(1). About the dataset detail.\n\nWe are willing to provide detailed information about our dataset. Our final dataset contains 100K high-quality perspective transformation videos, split into 90K/5K/5K for training/validation/testing. Specifically, our automated pipeline builds upon the approach described in Section 3.2. First, we start with expert images from multiple professional photography datasets, including GAIC, SACD, FLMS, FCDB, and Unsplash, totaling approximately 90K expert-composed images. Second, we adopt ViewCrafter to generate video sequences that transition from optimal to suboptimal perspectives, then reverse these sequences to obtain our training data. Third, PQA is applied to filter the data.\n\nWe will release the dataset and complete project code to promote this research area.\n\n>Q2 & W1(2). Red bounding boxes and homography transformations.\n\nWe would like to clarify that these components, including the red bounding boxes, feature matching, and homography transformations are **not part of the learning process** but rather serve as **auxiliary guidance tools** for practical user interaction.\n\nSpecifically, our implementation works as follows: We first draw a guidance box on the final optimal perspective frame, which is a rectangular box centered at the frame center. Then, using feature matching between the initial and final perspectives, we extract corresponding point pairs and estimate the homography matrix. We then apply this homography transformation to project this box onto the original image, creating a distorted box shape. As users physically move their camera following our video guidance, this box gradually changes shape and approaches a rectangular form when reaching the optimal perspective.\n\n>W1(4). \"Ours\" model identification.\n\nUnlike typical papers that focus on comparing specific models, we want to clarify that in our experiments, \"ours\" refers to our PPC pipeline rather than any specific model.\n\nIn our tables, we highlight the Wan2.1 14B model in gray to indicate it as our primary experimental configuration. We select this model based on its superior performance in our evaluations, and it is used to generate visualization results.\n\nOur paper presents a pipeline rather than modifications to the video generation model, where video generation models serve as implementation tools. \nWe extensively tested three SOTA video generation models (CogVideoX 1.5 5B, Hunyuan I2V, and Wan2.1 14B) to validate our pipeline.\n\n>Q4 & W2. About the human preferences and the inter-annotator reliability in PQA.\n\nWe appreciate the reviewer's concern. We provide detailed information about our human preference collection and inter-annotator reliability.\n\nOur PQA model training involves 8 expert annotators with professional photography backgrounds. For standard videos, each video is annotated by 3 annotators using pairwise comparison across three dimensions: Visual Quality (VQ), Motion Quality (MQ), and Composition Aesthetic (CA). The video pairs in our annotations consist of videos generated by different video models using the same input image. \n\nTo assess inter-annotator reliability, we employ **Fleiss' Kappa**, a statistical measure designed to evaluate agreement among multiple raters:\n\n| Metric | Score | Interpretation |\n|--------|--------|-------------|\n| VQ | κ = 0.78 | Substantial |\n| MQ | κ = 0.70 | Moderate |\n| CA | κ = 0.74 | Substantial |\n| Overall Multi-Annotator | κ = 0.71 | Substantial |\n\nWe can observe that our annotations demonstrate substantial consistency across three metrics.\n\n\n**About the Expert Bias.**\n\nTo address this, we conduct additional validation with broader user groups. Due to time constraints, we conduct a preliminary validation study with 28 amateur users who evaluated 20 randomly selected video pairs across three dimensions through questionnaire (different from the one in the rebuttal for Reviewer 4nUR). Expert annotations for these videos are determined by majority vote from our 3-expert panel.\n\nThe agreement analysis shows that amateur users aligned well with expert consensus: 91.2% agreed with experts on VQ, 85.8% on MQ, and 82.3% on CA (averaged across 20 videos). These agreement rates demonstrate reasonable alignment between expert and amateur preferences. \n\nWe plan to expand this validation study to include a larger and more diverse group of amateur users in future work.\n\n>Q5 & W3. About the visual attribute changes.\n\nWe thank the reviewer for this important observation. We would like to clarify our core motivation and our contribution to the visual attribute change.\n\n**Real-World Perspective Changes.**\nIn real-world photography scenarios, perspective transformations naturally introduce visual attribute changes, such as varying lighting conditions, altered subject-background relationships, and changes in depth of field. These changes are inherent to the physical nature of perspective adjustment. \nWhile these visual attribute variations are important to consider, our core motivation is to establish a **perspective-based compositional** pipeline that guides users in improving their shots through optimal perspective adjustments. \n\nEven so, during the model training process, we have made several efforts to minimize visual attribute distortions:\n\n**(1) Angle Limitation Strategy.**\nDuring data generation, we limit the maximum angle difference (20°) between the first and last frames of training videos. In our initial experiments without this limitation, the model often exhibited severe hallucinations by generating completely new scenes with drastically different lighting and exposure conditions.\n\n**(2) RLHF-Based Consistency Optimization.**\nUpon analyzing these cases, we discovered that while some videos exhibited attribute changes, their perspective transformations actually aligned well with human aesthetic preferences. This led us to introduce a second-stage RLHF training that relaxes rigid constraints and allows the model to learn from human preference feedback. \n\n**Quantitative Analysis of Visual Attribute Changes.**\nTo assess the visual attribute variations in our generated videos, we conduct measurements across key visual dimensions. Our analysis evaluated 1,200 videos:\n\n| Visual Attribute | Direct I2V | Base PPC | + Angle Limitation | + RLHF | Both |  |\n|-----|-----|-----|-----|-----|-----|-----|\n| **Exposure Variation (%)** |35.2|22.6|13.1|16.8|11.2| \n| **Color Shift (ΔLAB)** |9.7|7.2|4.2|5.1|3.8| \n| **Contrast Change (%)** |31.6|21.8|10.5|14.3|8.7|\n\nThese results demonstrate that our PPC methodology maintains reasonable visual attribute stability, and our additional control mechanisms provide further refinement.\n\nFurthermore, we are actively developing the **Unreal Engine 5-Based Data Generation Pipeline**. This game engine approach provides significantly more controlled environments for generating training data. Through perfect lighting control, we can eliminate the lighting inconsistencies that commonly occur in real-world 3D reconstruction by providing precise environmental lighting control. \n\n>W1(3). RLHF implementation clarification.\n\nThe reviewer's question about \"prompts used\" reflects a misunderstanding of our RLHF approach. Our RLHF implementation does not rely on text prompts, as the operational logic and purpose differ from typical text-based RLHF applications. Instead, our RLHF operates directly on image-to-video generation models, optimizing the perspective transformation quality.\n\nFor defining higher-quality and lower-quality videos, we clearly specify in our paper that win/lose video pairs are determined by our PQA model, which serves this dual purpose of both data filtering and RLHF preference determination. The PQA model evaluates videos across three dimensions (visual quality, motion quality, and composition aesthetic) to establish preference rankings, eliminating the need for manual preference annotations in the RLHF process.\n\n>W4(1). About the trajectory.\n\nThe \"trajectory\" mentioned in Line 120 refers to the camera motion trajectories used in our automated dataset construction (Section 3.2 in the paper). \n\n>W4(2). About the shared linear projection head.\n\nWe appreciate this insightful question about our architectural choice. While the reviewer's intuition about separate projection heads for different reward types is reasonable, our shared linear projection head design follows the **causal attention mechanism**. The true separation occurs at the attention level, not the projection level. Each token has already acquired fundamentally different feature representations through causal attention constraints. Therefore, a single shared linear head is sufficient to map these distinct token representations to their respective dimension scores."}]

Author rebuttal:

Meta-review: Final rating: 5. Accept/ 4: Borderline Accept/ 4: Borderline Accept/ 4: Borderline Accept. The paper received initial ratings of  Accept/  Borderline Reject/ Borderline Accept/  Borderline Reject, which introduces a 3D recomposition method that adjusts viewpoint—rather than just cropping—to rebalance subject relationships while preserving true spatial layout. The paper addresses missing data and metrics by (1) auto-building PPC datasets from expert photos, (2) generating videos that show suboptimal→optimal perspective changes, and (3) training a human-grounded Perspective Quality Assessment model. The system works without prompts or camera trajectories, guiding everyday users to improve composition. 

Reviewers agree the paper presents a novel and compelling idea (a view shared by all), supports it with extensive quantitative and qualitative experiments across diverse scenarios, and offers a well-designed pipeline. However, they find the validation thin—PQA appears empirical with no user studies or inter-annotator reliability—and several methodological details are under-specified (dataset size/splits, guidance integration, RLHF prompts, model identity). Key baselines are missing (state-of-the-art cropping-based and an image-only transformation), and practical fragility—artifacts, exposure/color shifts, hallucinations, perspective overshoot, and scale-related degradation—leaves real-world usability and generalizability uncertain.

After the rebuttal, all reviewers indicates their concerns were satisfactorily addressed and now lean toward acceptance, except reviewer rmgK, who still notes minor artifacts (e.g., structural distortion and loss of detail) in some visuals. The ACs  look into this and find that the authors proposed two mitigation strategies in the rebuttal, supported by additional quantitative evidence, which should reasonably improve visual quality.

Therefore, the area chairs agree to accept the paper and recommend the authors refine it according to the reviewers’ suggestions for the camera-ready version.
Decision: Accept (poster)