Title: Epipolar Geometry Improves Video Generation Models
Venue: NeurIPS 2025
Accepted: no
Keywords: diffusion models

epipolar geometry

latent video diffusion

video generation

3d geometry

3d computer vision

direct preference optimization
Abstract: Video generation models have progressed tremendously through large latent diffusion transformers trained with rectified flow techniques. Yet, despite these advances, these models still struggle with geometric inconsistencies, unstable motion, and visual artifacts that break the illusion of realistic 3D scenes. 3D-consistent video generation could significantly impact numerous downstream applications in generation and reconstruction tasks.
This work explores how simple epipolar geometry constraints can improve modern video diffusion models trained on internet-scale datasets. Despite their massive training data, these models often fail to capture the fundamental geometric principles underlying all visual content. While traditional computer vision methods are often non-differentiable and computationally expensive, they provide reliable, mathematically grounded signals for 3D consistency evaluation. We demonstrate that aligning diffusion models through a preference-based optimization framework using pairwise epipolar geometry constraints yields videos with superior visual quality, enhanced 3D consistency, and significantly improved motion stability.
Our approach offers an efficient alignment strategy that enforces established geometric principles without requiring end-to-end differentiability. Evaluation shows that our method outperforms baseline models and alternative alignment approaches across various metrics. By bridging the gap between data-driven deep learning and classical geometric computer vision, we present a practical method for generating more spatially consistent videos without compromising visual quality or requiring explicit 3D supervision.

Abstract

Video generation models have progressed tremendously through large latent diffusion transformers trained with rectified flow techniques. Yet, despite these advances, these models still struggle with geometric inconsistencies, unstable motion, and visual artifacts that break the illusion of realistic 3D scenes. 3D-consistent video generation could significantly impact numerous downstream applications in generation and reconstruction tasks. This work explores how simple epipolar geometry constraints can improve modern video diffusion models trained on internet-scale datasets. Despite their massive training data, these models often fail to capture the fundamental geometric principles underlying all visual content. While traditional computer vision methods are often non-differentiable and computationally expensive, they provide reliable, mathematically grounded signals for 3D consistency evaluation. We demonstrate that aligning diffusion models through a preferencebased optimization framework using pairwise epipolar geometry constraints yields videos with superior visual quality, enhanced 3D consistency, and significantly improved motion stability. Our approach offers an efficient alignment strategy that enforces established geometric principles without requiring end-to-end differentiability. Evaluation shows that our method outperforms baseline models and alternative alignment approaches across various metrics. By bridging the gap between data-driven deep learning and classical geometric computer vision, we present a practical method for generating more spatially consistent videos without compromising visual quality or requiring explicit 3D supervision.

1 Introduction

Video generation has witnessed remarkable progress in recent years, with newer models [1-6] producing increasingly realistic content from text and image conditions. This rapid advancement has spurred researchers to repurpose these powerful video models for broader applications, including animation [7], virtual worlds generation [8], and novel view synthesis [9].

Video diffusion models are trained on vast volumes of data, encoding rich priors about the visual world and its dynamics. Through extensive training, these models develop a strong understanding of object appearance, motion patterns, and scene composition. As a result, many recent works aim to utilize the priors from latent video diffusion models in various downstream tasks [10-12]. Despite this remarkable progress, these models still struggle to maintain perfect 3D consistency throughout generated sequences. Current video models often produce content with geometric inconsistencies, unstable motion, and perspective flaws, even though almost all training data is 3D consistent. Some approaches for enhancing 3D consistency rely on noise optimization [13], explicit guidance through point clouds [14, 15], or camera parameters [16]. Nevertheless, inaccurate control signals can constrain the model's generative capabilities, and the latent space optimization typical in diffusion training makes it difficult to compute direct geometric losses on the final outputs.

With the rising popularity of reinforcement learning for model alignment [17-19], post-training 38 alignment has recently gained more attention in diffusion model research as an alternative approach 39 to improve model capabilities. Methods such as VideoReward [20] have finetuned vision-language 40 models on a large-scale human preference data, enabling direct supervision through the reward model. 41 However, it relies on human-annotated motion quality scores (1 to 5), which can introduce noisy 42 signals into the training process and are expensive to collect. Human judgments about video quality 43 are inherently subjective and may not consistently capture the geometric principles that ensure proper 44 3D consistency. The gap between subjective human evaluations and objective geometric requirements 45 creates an opportunity for alignment methods that leverage more mathematically grounded metrics 46 for video quality assessment. 47

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We propose a simple approach that bridges modern video diffusion models with classical computer vision algorithms. Rather than incorporating explicit 3D guidance during generation, we use wellestablished non-differentiable geometric constraints as reward signals in a preference-based finetuning framework. Specifically, we leverage an epipolar geometry constraint: assessing 3D consistency between frames. By sampling multiple videos conditioned on the same prompt, we generate diverse camera trajectories that typically vary in geometric coherence. The quality of these trajectories is well-captured by epipolar geometry metrics, providing a reliable signal for identifying which generations better adhere to projective geometry principles. This insight enables us to rank videos based on their adherence to epipolar constraints, creating training pairs that guide the model toward improved geometric consistency.

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Our method implements this through Direct Preference Optimization (DPO) [17], requiring only relative rankings rather than absolute reward values. This approach bypasses the difficulties of directly using non-differentiable computer vision algorithms in the training loop. DPO only needs to determine which output better adheres to the geometric constraints. By finetuning the model to prioritize generations that satisfy these classical geometric constraints, we guide it towards generating inherently more 3D-consistent videos, without restricting its creative capabilities or requiring explicit 3D supervision. As shown in Figure 1, this results in enhanced 3D consistency, smoother camera trajectories, and fewer artifacts compared to the baseline model.

While simple in nature, this paper shows that a basic geometric constraint, described in 1982 [21], can recover what video models fail to do, even after large-scale training on billion-scale data: 3D consistency. In summary, the key contributions are as follows:

Epipolar Geometry Optimization: We introduce a method for finetuning video diffusion models using epipolar geometry constraints as reward signals, particularly leveraging the Sampson distance to enhance 3D video consistency without needing differentiability. The models finetuned with the simple yet reliable signal from classical computer vision algorithms achieve superior consistency and quality, significantly reducing artifacts and unstable motion trajectories in generated content. Our approach demonstrates that aligning models with fundamental geometric principles leads to visually superior results while preserving the model's ability to generate diverse and creative content.

Comprehensive Evaluation Framework: Wedevelop an extensive evaluation protocol that measures both perceptual quality and 3D consistency and adherence to projective geometry principles across diverse generation scenarios. We evaluate text and image-to-video finetuned models, exploring the impact of geometry-aware finetuning on a large set of metrics.

Large-Scale Preference Dataset: We create and release a large dataset of over 162,000 generated videos annotated with 3D scene consistency metrics, enabling further research in geometry-aware video generation. This dataset includes diverse prompts spanning natural landscapes, architectural scenes, and dynamic environments, each with multiple video generations.

2 Related Work

We structure the related work section into generative models and post-training methods to adapt them.

2.1 Video Generation Models

Recent advances in video generation have been dominated by closed-source models developed by 87 well-resourced technology companies. These models, trained on large proprietary datasets with 88

BaseModel

EpipolarGeometryOptimization

computational resources beyond academic reach, have demonstrated remarkable capabilities while 89 revealing limited architectural details. Notable releases include OpenAI's Sora [1], which marked a 90 significant leap in long-form video synthesis; Runway's Gen-2 and Gen-3 [22]; Luma AI's video 91 models [23]; Pika Labs models [6]; and Google DeepMind's Veo series [2]. While these systems 92 produce impressive results, their closed nature limits opportunities to finetune them or apply them to 93 other vision tasks. 94

More recently, open-source large latent diffusion models have become available, increasing interest 95 in improving video generators. Stable Video Diffusion [24] developed efficient training strategies 96 for latent video diffusion. Hunyan-Video [5] presented a systematic approach to scaling models, 97 LTX-Video [25] introduced optimizations for real-time generation, and Wan-2.1 [4] introduced an 98 efficient 3D Variational Autoencoder [26] with expanded training pipelines. Wan-2.1 offers models 99 for text-to-image and video-to-image in 1.3B and 14B parameter versions, enabling researchers to 100 explore adaptation techniques for various downstream tasks. 101

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These video diffusion models are trained on enormous data volumes covering more content variety than specific applications need, making domain-aware alignment valuable for specialized tasks. Geometry-aware finetuning allows general-purpose models to maintain creative flexibility while ensuring adherence to physical principles like 3D consistency. V3D [12] finetunes models to generate 360 orbit frames for 3D reconstruction, while VideoReward [20] introduced a framework for reinforcement learning-based video model alignment. However, prior methods rely on subjective human preferences or vision language models [27] trained to mimic them. In contrast, our approach optimizes against mathematical rules from epipolar geometry, providing a clean signal that aligns models with fundamental 3D consistency principles rather than subjective judgments.

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2.2 Diffusion Models Alignment

Since image and video latent diffusion models are trained on internet-scale noisy data, efficient 112 finetuning, and alignment strategies have emerged as an active research area. Latent image diffusion 113 models [28, 29] finetune models on data highly ranked by the aesthetics classifier [30]. DRAFT [31] 114 and AlignProp [32] further explore this paradigm by tuning the diffusion model to maximize the 115 reward function directly. DPOK [33] and DDPO [34] expand the paradigm to introduce distributional 116 constraints. Diffusion-DPO [35] introduces the Direct Preference Optimization algorithm into 117 diffusion model alignment. In contrast to other approaches, DPO does not require direct access 118 to the reward model and can be trained with only pairwise preference data. Additionally, this 119 eliminates the need to decode the final denoised sample, which can be finetuned directly in latent 120 space, significantly improving training efficiency. Recently, VideoReward [20] adapted Diffusion121 DPO for video alignment, effectively aligning video generation with human preferences. Yet, all these 122 approaches focus on optimizing for subjective and noisy human evaluation. Lately, DSO [36] employs 123 DPO to align 3D generators with physical soundness, and PISA explicitly [37] improves the physical 124 stability of video generators with a multi-component reward function. Our method leverages classical 125 computer vision algorithms to provide objective, mathematically grounded preference signals based 126

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GenerateVideoData

Epipolar Geometry

Inference

Optimization

PretrainedLatentVideoDiffusion

Finetuned LatentVideoDiffusion(LoRA)

pref

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AddNoise

GenerateVideosWithUpdated

E0,E1,...

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Upred

w0-Upred（x,t)²ref（x，t）2

SampsonEpipolarError

{=-pred（xt）²-ref（xt）²2

3DConsistentScene

on epipolar geometry, resulting in more reliable and consistent alignment with 3D physical principles. However, creating an explicit, robust, differentiable geometry reward model is challenging due to the complexity of accurately modeling and evaluating 3D consistency across diverse scenes. Our method addresses these challenges by leveraging classical computer vision algorithms to provide objective, physically grounded preferences based on epipolar geometry, resulting in consistent alignment.

3 Method

We aim to align pretrained video diffusion models to generate geometrically consistent 3D scenes from text or image prompts. To address this, we propose an alignment strategy that leverages classical epipolar geometry constraints within a preference-based optimization framework. Traditional reinforcement learning approaches [18, 38] require explicit reward functions and access to final samples which is impractical for video models due to the absence of robust differentiable reward models and the prohibitive computational cost of the denoising process. Our key observation is that while classical epipolar geometry constraints do not produce a smooth, globally comparable loss surface across different scene types (e.g., indoor vs. outdoor scenes may exhibit different absolute error magnitudes due to variations in matchable feature counts), the relative intra-prompt error measurements remain consistent. When generating multiple video sequences with fixed conditioning, the stochastic nature of diffusion sampling produces outputs with varying degrees of geometric consistency. Epipolar error metrics are an effective tool to quantify relative 3D consistency, with higher values reliably indicating lower geometric consistency. This finding aligns with the direct preference optimization (DPO) paradigm, which requires only a relative metric to determine preference between output pairs rather than absolute reward values. The pairwise comparison nature of DPO eliminates the need for a globally normalized reward function, instead leveraging the reliable local ranking provided by epipolar geometry measurements to guide model alignment toward more geometrically consistent video generation.

3.1 Objective Function

Given the pretrained video generator p ref that takes a text prompt and an optional first frame conditioning I and generates video samples x 0 ∼ p ref ( x 0 | T, I ∗ ) , where I ∗ ∈ { I, ∅} we want to learn the model p θ which is optimized to generate 3D-consistent video sequences. The one choice would be to optimize it with the following objective:

where r ( x 0 ) is a reward function that outputs 3D consistency scores, D c are samples from the 156 reference model. The reward is maximized while the optimized model p θ is kept close to the 157 reference model p ref via a KL-divergence term weighted by the hyperparameter β . This formulation 158 directly encourages the model to generate videos with improved geometric consistency. However, 159

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this formulation presents a few critical practical challenges. First, the reward function r ( x 0 ) relies on classical computer vision algorithms that are non-differentiable, making direct gradient-based optimization infeasible. Second, evaluating this reward function requires complete video generation and subsequent geometric analysis, which is highly time-consuming for training large video diffusion models. These constraints make traditional reinforcement learning approaches impractical for our setting, and motivate our adoption of Direct Preference Optimization (DPO) [17, 35], which was originally designed for scenarios where direct reward optimization is similarly challenging.

Assuming a fixed dataset of D ( { x, x w 0 , x l 0 } ) which consists of condition c (text, image), and a pair of samples from the p ref such that x w 0 has higher reward value than x l 0 ( x w 0 ≻ x l 0 ).

Diffusion-DPO [35] aligns diffusion models with human preferences by directly solving eq. (1) analytically. It interprets alignment as a classification problem and optimizes a policy to satisfy the preferences through supervised training. The Diffusion-DPO objective L DD ( θ ) is given by:

where x ∗ t = (1 -t ) x ∗ 0 + t ϵ ∗ , ϵ ∗ ∼ N (0 , I ) . The superscript ∗ ∈ { w,l } denotes either w for the sample with a higher score or l for a sample with a lower score, ϵ ∗ is a ground truth or predicted noise by a diffusion model. The expectation is taken over samples { x w 0 , x l 0 } ∼ D and the noise schedule t .

For rectified flow models [39-41] the noise vector ϵ ∗ is related to the velocity field v ∗ following [20]: 175

The final Flow-DPO loss [20] is formulated as:

where β t = β (1 -t 2 ) .

Intuitively, minimizing this loss encourages the model to improve its denoising performance on preferred samples x w t relative to less preferred samples x l t [20, 35]. This guides the predicted velocity field v θ to align more closely to videos exhibiting better 3D consistency while diverging from those with poorer geometric coherence.

3.2 3D Consistency Metric

We evaluate the 3D consistency of generated videos by validating how well they satisfy epipolar geometry constraints. Epipolar geometry represents the intrinsic projective relationship between two views of the same scene, depending only on the camera's internal parameters and relative positions. In perfectly consistent 3D scenes, corresponding points across different viewpoints must adhere to these geometric constraints.

For any two corresponding points x in one frame and x ′ in another, the epipolar constraint x ′ T Fx = 0 must be satisfied, where F is the fundamental matrix. This constraint ensures that a point in one view must lie on its corresponding epipolar line in the other view. The fundamental matrix encapsulates the geometric relationship between the two camera poses. It can be formulated as F = [ e ′ ] × P ′ P + , where P and P ′ are the camera projection matrices, P + is the pseudo-inverse of P , and e ′ is the epipole in the second view.

Given a pair of frames x i and x j from a generated video, we first compute a set of point correspondences using SIFT [42] feature matching. While we validate the method with a simple, robust

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handcrafted descriptor, the pipeline can also leverage more recent learned descriptors [43-46]. These correspondences provide a robust set of matching points between the different viewpoints. We then estimate the fundamental matrix using the normalized 8-point algorithm within a RANSAC [47] framework to handle outliers.

Once we have estimated the fundamental matrix, we can measure the geometric consistency using the Sampson epipolar error [21]:

The Sampson error provides a first-order approximation to the geometric distance between a point and its epipolar line. Lower Sampson error values indicate better adherence to projective geometry constraints and, thus, more consistent 3D structure in the generated videos.

3.3 Implementation Details

We conduct experiments with a state-of-the-art open-source video diffusion model called Wan2.1 [4], which possesses 1.3 billion parameters. Our approach is validated in text-to-video and image-to-video generation setups to demonstrate versatility across conditioning types.

Offline Dataset Generation Since our method focuses on 3D-consistent scene generation, we require videos of static scenes with dynamic camera movements. We extract text prompts from the DL3DV [48] and RealEstate10K [49] datasets, provided by [50], containing a wide variety of indoor and outdoor scenes. We generate three videos per caption to ensure sufficient variation in 3D consistency quality, as our preliminary experiments showed that pairs generated from just two samples often lacked meaningful geometric differences. This approach balances computational efficiency with training data quality. We filter put near-static videos to prevent the model from learning a degenerate solution of minimizing camera movement to satisfy epipolar constraint. In total, we generate 24,000 videos for text-to-video and 30,000 videos for image-to-video training, requiring approximately 1,980 GPU hours on NVIDIA A6000s.

Training Configuration Given the computational demands of fine-tuning large video diffusion models, we implement our approach using Low-Rank Adaptation (LoRA) [51] with rank r = 64 and α = 128 . This strategy offers the additional benefit of eliminating the need to store the reference model separately in memory, since the base model with the adapter disabled naturally serves as p ref during training. We train with a batch size of 32 for 10,000 iterations using the AdamW [52] optimizer with a learning rate of 5 × 10 -6 and 500 warmup steps. The finetuning takes 2 days on 4 A6000 GPUs.

4 Experiments

We assess the effectiveness of our epipolar-aligned video diffusion model compared to baseline approaches and evaluate its impact on scene consistency, visual quality, and prompt alignment. Our evaluation setup consists of 200 videos extracted from the test sets of DL3DV [48] and RealEstate10K [49] datasets, covering a diverse range of indoor and outdoor scenes. To thoroughly test geometric consistency under challenging conditions, we amplify the complexity of camera motion by augmenting prompts with motion-specific phrases (e.g., "orbiting around," "zooming in," "panning across"). We evaluate our model across three complementary benchmarks: (1) the VideoReward benchmark [20], which measures general video generation quality; (2) VBench [53], which provides standardized metrics for temporal consistency and visual fidelity; and (3) our custom suite of 3D consistency metrics based on epipolar geometry constraints. This multi-protocol evaluation approach allows us to comprehensively assess the generated videos' perceptual quality and geometric consistency.

Figure 3 shows some qualitative examples. Before our fine-tuning, the videos often contain morphing objects or inconsistent geometry.

4.1 VideoReward Benchmark Evaluation

The VideoReward [20] benchmark evaluates videos across Visual Quality, Motion Quality, and Text Alignment dimensions using a vision language model [27] finetuned on human preferences.

A store aisle showcasing various kitchen appliances，including instant pot and microwave oven,with promotional displays.Fluid，dynamic camera with with dynamic perspective

A modern hallway with decorative shelves，potted plants,anda signboard for wedding banquet and engagement party services.Fluid,dynamic camera with changes in perspective.

Amodern buildingwitha curvedfacade and large windows,surrounded by greenery andapavedwalkway，The camerasmoothlypushesin

Original

EpipolarGeometryOptimization

Annotators select preferences between video pairs, and a VLM simulates these judgments. We use 243 the resulting pairwise scores to compute win rates of our finetuned model versus the baseline. We 244 also compare against a model aligned with MET3R [54] to assess how our epipolar geometry metric 245 compares to modern 3D vision metrics [55]. Table 1 presents the results of this evaluation. Our 246 text-to-video model significantly outperforms both the baseline and MET3R-based models across all 247 metrics, with win rates of 72.0%, 71.0%, and 55.0% for Visual Quality, Motion Quality, and Text 248 Alignment respectively. This demonstrates that alignment with epipolar constraints enhances not 249 only motion quality but also visual fidelity by reducing artifacts. The image-to-video model, trained 250 with more conservative hyperparameters to minimize baseline deviation, still shows meaningful 251 improvements over both the baseline and MET3R-aligned models in most categories. 252

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4.2 VBench Benchmark Evaluation

VBench [53] introduces a comprehensive benchmark suite for video generative models. It consists of 254 a large set of metrics across multiple dimensions, facilitating fine-grained and objective evaluation. 255 We provide the results on five metrics related to visual and motion quality. Background Consistency 256 evaluates the temporal consistency of the background scenes by calculating CLIP [56] feature 257 similarity across frames. Aesthetic Quality evaluates the artistic and beauty value humans perceive 258 towards each video frame using the LAION aesthetic predictor [30], measuring such concepts as 259 layout and photo-realism. Temporal Flickering extracts static frames and computes the mean 260 absolute difference across frames. Motion Smoothness validates whether generated motion follows 261

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the physical law of the real world. It utilizes the motion priors in the video frame interpolation model [57] to evaluate the smoothness of generated motions. Finally, Dynamic Degree employs RAFT [58] to estimate the degree of dynamics in synthesized videos. The results are presented in Table 2. We compare the finetuned Wan-2.1 [4] models to the baseline. The text-to-video model improves the scores across all metrics; however, it sacrifices the dynamic degree by a bit. Nevertheless, the other benchmarks Table 3 and Table 1 demonstrate that the finetuned model generates comparable or superior dynamics. The image-to-video model, being finetuned, reduces the amount of edge cases that perform comparably to the baseline.

4.3 3D Geometry Evaluation

Last, we evaluate the direct impact of the finetuned models on 3D geometry metrics. Table 3 shows 271 results across multiple geometric consistency measures. We assess Sampson error (our primary 272 optimization target), MET3R score [54], the realism of Perspective Fields [59] and Motion Level. 273 Perspective Fields classifier [60] evaluates the realism of image perspective fields. Since the metric 274 is image-level, we compute the mean metric across all frames. Additionally, we validate whether 275 the models tend to produce nearly static videos by computing the mean SSIM score between the 276 first and all the other video frames, hence, high scores for static content. Models finetuned with 277 epipolar geometry constraints show significant improvement in Sampson distance (33% reduction in 278 text-to-video), while matching MET3R-optimized models on their own metric. This confirms that 279 classical epipolar geometry provides a cleaner optimization signal than learned metrics, which show 280 only modest self-improvement due to noise when evaluating generated content. 281

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4.4 Comparison with Other Fine-tuning Techniques

We compare four finetuning strategies as shown in Table 4: Supervised Finetuning (SFT), Flow-based Reward-Weighted Regression (Flow-RWR) [20], Direct Reward Optimization (DRO) [36], and our proposed DPO with Sampson Error. SFT directly optimizes for minimal epipolar error but struggles without negative samples to distinguish consistency levels. Flow-RWR weights samples by reward values but suffers from inconsistent absolute metrics, while DRO eliminates reference model queries but deviates substantially from the baseline capabilities. Our approach outperforms all alternatives, achieving the highest win rates in Visual Quality (72.0%), Motion Quality (71.0%), and Overall score (73.0%). This demonstrates that preference-based optimization with geometric constraints provides more effective guidance than approaches relying on absolute metrics or unconstrained optimization. Notably, our method achieves these improvements while maintaining the generative flexibility of the original model, allowing it to produce diverse outputs that satisfy both creative and geometric requirements simultaneously.

5 Limitations and Broader Impact

Our approach primarily focuses on static scenes with dynamic camera movements, aligning well with applications in 3D reconstruction and novel view synthesis. Adapting this method to scenes with dynamic objects would require modifying the training pipeline to separately model and evaluate object motion and camera movement. Additionally, epipolar geometry constraints assume point correspondences coming from a static scene under camera motion, limiting effectiveness for scenes with independent object movement or non-rigid deformations where a single fundamental matrix cannot explain all correspondences. Video generation models may be misused to produce realistic but deceptive content, contributing to the spread of misinformation, political manipulation, and erosion of public trust. Furthermore, the computational resources required to train such models raise environmental concerns and may exacerbate inequalities in access to advanced AI technologies. Geometry-aware video generation can facilitate various 3D vision tasks, including scene reconstruction, SLAM, and visual odometry. By improving geometric consistency in generated videos, our method produces more realistic and usable synthetic data for training computer vision systems. This advances applications in robotics and autonomous navigation, where accurate spatial understanding is crucial. The integration of classical geometry principles with modern generative models represents a promising direction for enhancing AI systems with stronger physical world understanding.

6 Conclusion

We have presented a novel approach for enhancing 3D consistency in video diffusion models by 313 leveraging classical epipolar geometry constraints as preference signals. Our work demonstrates 314 that aligning modern generative models with fundamental geometric principles can significantly 315 improve the spatial coherence of generated content. The robust, mathematically grounded signal 316 from simple Sampson error calculations provides clear guidance without requiring complex 3D 317 supervision or differentiable rewards. The resulting models generate videos with notably fewer 318 geometric inconsistencies and more stable camera trajectories while preserving creative flexibility. 319 This work highlights how classical computer vision algorithms can effectively complement deep 320 learning approaches, addressing limitations in purely data-driven systems and improving generated 321 content quality through adherence to fundamental physical principles. 322

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

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

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16. Declaration of LLM usage

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

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes to improve the 3D consistency of video generation through the epipolar constraint, while the optimization is based on reinforcement learning, i.e., diffusion (flow-matching) DPO. Specifically, they employ the SIFT feature matching to achieve the fundamental matrix, and then use the Sampson epipolar error as the reward. The fine-tuning is implemented with LoRA. Comprehensive experiments and qualitative videos submitted in the supplementary show the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The idea of leveraging 3D consistent metrics to align the video model through DPO is interesting.\n2. This paper includes comprehensive experiments, such as the VideoReward benchmark, VBench, 3D consistency metrics, and the comparison among different fine-tuning strategies.\n3. The paper is well-organized, while the Sampson epipolar error used in this work is convincing.\n\nWeaknesses:\n1. Since this paper uses the DPO learning, which addresses the non-differentiable issue, more heuristic metrics should also be discussed and compared. For example, as this paper utilized SIFT and feature matching to get the fundamental matrix, many other matching metrics should be considered (number of matching points, SfM points,  matching confidence, and re-projection error), while the epipolar error is just a special case of them. This paper lacks these discussions to claim the superiority of the epipolar error.\nSince DPO does not care about the reward value, the authors should provide more evidence to demonstrate that the Sampson epipolar error is the best choice.\n\n2. Another concern is whether the proposed epipolar constraint will potentially hinder the dynamicity of the video model, as shown in Table 2. Intuitively, smaller movements lead to better 3D consistency. However, the authors did not include any penalty for small camera movement, which may make the model tend to synthesize videos with small but consistent camera motions. \n\n3. Why training and evaluation are limited only on DL3DV and Real10K? Theoretically, one advantage of reinforcement learning is that it can learn any results generated by itself without the limitation of real-world datasets, resulting in strong generalization across various domains. However, this work only focuses on real-world multi-view images, which is very confusing. Furthermore, the authors should provide VBench results based on natural videos to show that the generalization of the LoRA fine-tuned model is preserved.\n\n4. Some introductions about the Diffusion/Flow-DPO are not sufficient. The meaning of $\\sigma$ in Eq(2), Eq(4) is not mentioned. When the DPO is starting ($v_{ref}=v_{\\theta}$), the loss definition would suffer from a numerical issue.\n\n5. Some presentations of the figures should be improved. The epipolar lines drawn in Figures 1 and 2 are confusing. I suggest highlighting the epipolar \"error\" between the source points and target lines in the figures across different generations.\n\nQUESTIONS:\nMy current inclination is to vote for the acceptance of this paper, though I still have significant concerns. These concerns are primarily rooted in the first three weaknesses, which critically informed my final assessment.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work explores using epipolar geometry constraints to post‐train latent video diffusion models for enhanced 3D consistency. The main contributions are:\n\n-Leveraging pairwise Sampson error as a reward in a Direct Preference Optimization (DPO) to rank and fine‐tune generated video samples for improved geometric coherence \n-Introducing a classical epipolar errornthat requires only relative comparisons, thereby avoiding the need for explicit 3D supervision\n-Constructing a dataset of over 162 K generated video pairs annotated with epipolar consistency metrics for downstream alignment and evaluation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- Employing epipolar constraints as a preference signal bridges deep generative models with mathematically grounded 3D principles\n- Evaluation spans perceptual (VideoReward, VBench) and geometry‐specific (Sampson error, Perspective Fields) measures\n\nMajor Weaknesses\n-While Sampson error is effective, the paper lacks comparison to other geometric criteria (e.g., symmetric epipolar error, learned multi‐view consistency scores) to justify its optimality.\n-Details on the human‐in‐the‐loop VideoReward comparisons (e.g., number of raters, agreement levels) are sparse.\n-The preference dataset and evaluation rely exclusively on synthetic videos generated by the same base model (Wan-2.1). Without validation on external datasets or unseen models, it’s unclear if the approach generalizes beyond this\n-Key methodological details (e.g., choice of DPO hyperparameters, criteria for video filtering) are scattered or missing.\n-The authors should report results for the base video diffusion model (e.g., Wan-2.1) without any epipolar fine-tuning. Also a control variant that injects the same number of LoRA parameters but uses a zero-reward DPO. This will demonstrate that performance gains stem specifically from the geometry signal rather than simply adding parameters or preference tuning.\n-Provide timing benchmarks breaking down diffusion sampling, epipolar error computation, and DPO update on a single GPU. Compare this end-to-end runtime to a one-pass LoRA baseline, so readers can assess the practical trade-off between improved consistency and added cost.\n\nQUESTIONS:\nsee comments above\n\nLIMITATIONS:\nsee comments above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents an attempt to use traditional geometrical constraints to perform post-training fine-tuning of video diffusion models. Specifically, it studies using epipolar line constraints to enforce geometrical consistency in the generated videos by using DPO. Experiments are conducted to show the effectiveness of the proposed framework.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n\n- This paper pivots on a very important direction of enforcing physical inductive biases in video generative models. The presented approach is simple, effective, and clear, and shows a strong message of the importance of physical priors in finetuning video models. Many potential follow-ups can be inspired by this work, including adding even more physical non-differentiable biases to the video models, including physical stability, energy conservation, etc.\n- The contributed dataset might be useful in other areas, like aligning or evaluating VLLMs with physical plausibilities.\n\n**Weaknesses**:\n\n- The improvement over quantitative 3D consistency metrics is not significant, especially for image-to-video models. For MET3R metrics, the proposed models are even worse than the baseline model.\n- 3D consistency metrics should also be evaluated for Table 4 to provide a more comprehensive study on different fine-tuning strategies.\n- Only comparing metrics like Sampson distance or MET3R is not enough to evaluate the 3D consistency. 3D reconstruction should be performed on the generated videos (like, reconstructing a NeRF or 3DGS), and metrics including reprojection error should be evaluated. Both qualitative and quantitative results should be reported on the 3D reconstruction quality.\n- Only one model (Wan 2.1) is evaluated in the experiments. More models should be evaluated to show the generalizability of the proposed framework.\n- It is not clear how the selection of feature extractors would influence the results. If using feature extractors like SuperGlue or Loftr, would the results be better?\n- It is not clear how the proposed framework compares against methods like VPO (Cheng et al., VPO: Aligning Text-to-Video Generation Models with Prompt Optimization).\n\nQUESTIONS:\n- The baseline results from Wan, as shown in the supplementary materials, look too bad to me. My impression is that Wan can generate fairly good videos. Are the authors cherry-picking failure cases from Wan to showcase the advantages? Please elaborate on the comprehensive policy on selecting demos.\n- L230: How are extracted videos used to evaluate generated content? More details should be elaborated here.\n\n- Some minor comments:\n  - All image resolutions in the figures are pretty low. Maybe there are some issues with PDF compression.\n  - L167: broken sentences?\n\nLIMITATIONS:\nThe epipolar constraints assume the camera intrinsics are not distorted. This prevents the video generative models from generating videos like egocentric ones.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
This paper uses classical epipolar geometry constraints as the rule to guide the video generation model.
It first uses Sampson error calcualations to rank the generated videos.
Then, DPO is used to further optimize the video generation models.
After the procedures, the models can produce videos with better geometric consistency.

STRENGTHS AND WEAKNESSES:
Strengths
1. The use of epipolar geometry constraints as a guiding principle for DPO is intriguing. This approach is both intuitive and promising.
2. The experiments demonstrate enhanced geometric consistency.

Weaknesses
1. The accuracy of the Sampson epipolar error might not be sufficient. Is it adequate for DPO? What if the two generated videos both have low epipolar accuracy?
2. Could the selection process introduce bias? For instance, some scenes might exhibit high epipolar accuracy while others show lower accuracy. This could lead to category bias after training.
3. I'm curious why the training was conducted on the DL3DV and RealEstate10K datasets instead of general videos. Wouldn't this limit the model's generalization ability?
4. What if the original methods fail to produce geometrically aligned videos? Can this method still offer improvements?
5. From the attached results, I'm curious why the original video quality appears so low. Based on the reviewer's experience, Wan-2.1-T2v typically delivers much better video quality than what is shown.
6. According to Table 3, the improvements seem limited, except for the Sampson Distance. It appears that the improved Sampson Distance results from its use in ranking. Thus, the method might not genuinely enhance overall geometric accuracy.

QUESTIONS:
See weaknesses

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 3
DATE: 2025-07-03