CamI2V: Camera-Controlled Image-to-Video Diffusion Model

Authors: Guangcong Zheng, Teng Li, Rui Jiang, Yehao Lu, Tao Wu, Xi Li
Primary area: generative models
Keywords: Camera Control, Image-to-video, Video Diffusion Model

Abstract:
Recent advancements have integrated camera pose as a user-friendly and physics-informed condition in video diffusion models, enabling precise camera control. In this paper, we identify one of the key challenges as effectively modeling noisy cross-frame interactions to enhance geometry consistency and camera controllability. We innovatively associate the quality of a condition with its ability to reduce uncertainty and interpret noisy cross-frame features as a form of noisy condition. Recognizing that noisy conditions provide deterministic information while also introducing randomness and potential misguidance due to added noise, we propose applying epipolar attention to only aggregate features along corresponding epipolar lines, thereby accessing an optimal amount of noisy conditions. Additionally, we address scenarios where epipolar lines disappear, commonly caused by rapid camera movements, dynamic objects, or occlusions, ensuring robust performance in diverse environments.
Furthermore, we develop a more robust and reproducible evaluation pipeline to address the inaccuracies and instabilities of existing camera control metrics. Our method achieves a 25.64\% improvement in camera controllability on the RealEstate10K dataset without compromising dynamics and generation quality, also demonstrating strong generalization to out-of-domain images. Training and inference require only 24GB and 12GB of memory, respectively, for 16-frame sequences at 256×256 resolution. We will release all checkpoints, along with training and evaluation code. Dynamic videos are available for viewing on our supplementary anonymous web page.

Decision: reject
Reviewer scores: 6, 6, 5, 5

Reviews and discussion:
<thread>
creation date:
1730466241110

thread_type:
official_review

<thread_object>
creator: reviewer_iyw3

<content>
summary:
This paper retrofits the text-guided video diffusion model with a camera control module, enhancing the camera trajectory controllability and 3D consistency for image animation. 

By representing a pinhole camera model as a bundle of ray-level 3D embeddings through Pl&uuml;cker coordinates, the denoising process is conditioned on camera poses in a _clean_ and expressive manner. To facilitate multi-view 3D reasoning and efficiency, the epipolar attention mechanism is developed, which only aggregates cross-view information within a limited region along the epipolar line. 

The authors test the method on RealEstate10K and out-of-domain datasets using an improved evaluation pipeline, and demonstrate its superiority over other alternatives.

strengths:
- The idea of introducing epipolar geometry to the temporal attention is straightforward and well-motivated, which allows the model to explicitly reason about the 3D world.
- It is interesting and technically sound to configure register tokens for degenerate cases where no corresponding epipolar lines are detected.
- Experiments on RealEstate10K and in the wild have shown that the proposed method improves the camera motion controllability and stability.

weaknesses:
- One major concern is that the proposed method is somewhat lack of novelty. The epipolar attention mechanism is not new [1], while the Pl&uuml;cker embedding is adopted from [2].  There are some impressive innovations, e.g. the concept of _clean/noised conditions_ and the register tokens, but related exposition and analysis could be more thorough. Please see Q1-5 in the question section for details.
- In the conclusion, the authors announce that their method _significantly_ improves the controllability and stability (Line 534-535), which may be an overstatement. In Tab.1, the advantage of CamI2V (the proposed method) over CameraCtrl is kind of minor, and in Tab.2, the introduction of the epipolar attention quantitatively appears to bring in only limited improvement. It would be nice to include additional (qualitative) results that demonstrate the effectiveness of each contribution. Also see Q6.
- One contribution that the authors adapt GLOMAP for more robust evaluation pipeline raises another concern. It is not convincing that GLOMAP can effectively address limitations such as low resolution and dynamic scenarios. More details and experimental comparisons would be helpful to demonstrate the merits of the proposed evaluation protocol.

[1] Hung-Yu Tseng, et al. "Consistent View Synthesis with Pose-Guided Diffusion Models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023.

[2] Hao He, et al. "CameraCtrl: Enabling Camera Control for Text-to-Video Generation." arXiv preprint arXiv:2404.02101. 2024.

questions:
1. Elaboration on the insight of the _clean/noised conditions_. The Pl&uuml;cker embeddings and the proposed epiplolar attention are regarded as clean conditions, but no further clarification is given. It would be preferable to include some mathematical analysis on how clean conditions can reduce uncertainty more effectively than noisy conditions, as the current perspective seems just intuitive and empirical rather than theoretically supported.
2. I appreciate the idea of reserving register tokens for special cases, but the authors fail to demonstrate its effectiveness through essential ablation studies. 
3. In the epipolar attention part, a hard mask strategy is employed, which filters out pixels far from epipolar lines, while [1] proposes the epipolar weight matrix (a form of soft masks). It would be interesting to draw a comparison between them. The hard mask strategy might be computationally more efficient, so for example, more specific details about the computational complexity or running time would be beneficial.
4. The epipolar geometry is based on the assumption that observations are all static. Wouldn't the proposed epipolar attention incur degradation in cases involving dynamic objects?
5. The multiple guidance scale is not only similar to [2] but also lacks explanation and experimental support. The authors should reconsider whether to count it as one contribution (Line 133-134).
6. Limited qualitative results. Camera trajectories used in supplementary materials are similar and relatively simple. Diverse camera trajectories for evaluating are in great need. 

- The authors may have to check the citation in Line 208-209: we follow _(Tseng et al., 2023)_ to represent cameras as ray bundles. 
- In the top middle of Fig. 4, what do the "Project" module and its learnable weights refer to? 
- Please check the format of notations. Make sure they are consistent throughout the paper. For example, the latent code should be in bold: $\mathbf{z}_t$.

[1] Hung-Yu Tseng, et al. "Consistent View Synthesis with Pose-Guided Diffusion Models." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023.

[2] Jinbo Xing, et al. "DynamiCrafter: Animating Open-domain Images with Video Diffusion Priors." Proceedings of the 18th European Conference on Computer Vision (ECCV). 2024
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creation date:
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thread_type:
official_review

<thread_object>
creator: reviewer_yhfg

<content>
summary:
This paper introduces CAMI2V, a camera controlled I2V model that integrates several camera-based components, such as epipolar attention and plucker embeddings, image conditions and text prompts.

strengths:
The paper is easy to follow, figures are intuitive and look good. 

Experiments shows the model outperforms all available state-of-the-art works.

weaknesses:
Novelty of the paper: The contribution of this paper highly resembles CamCo, which is released in June (3.5 months prior to the submission deadline). Both methods use plucker embedding, epipolar lines, and are aimed for image-2-video model. The paper mentioned that CamCo does not supports video trajectories with non-overlapping frames, and the introduction of register token alleviate this problem. Yet this can be  considered as a relatively minor improvement, and is not well supported by experiments. While CamCo does not release its code for reproduction, an ablation study on the token (comparing to other trivial techniques, such as averaging all K/V together), might be helpful to show its effectiveness. Aside from CamCo, CVD (Kuang et.al. 2024) also applies the epipolar attention in its cross-view modules, and CameraCtrl/CVD both use plucker embedding. All these methods challenge the novelty of this work. 

While many of the components proposed by the paper are originated from other prior works, the paper spent a huge portion of space to explain these components, overcomplicating the model itself. For example, in Figure 1/2 the authors try to analyze the differences between image/text conditions and noisy latents and show the importance of the epipolar attention, and later provides very detailed calculation on how to compute the attention maps. These contents are somehow redundant since they are already been proposed in prior works. 

The author also claims the improvement of the evaluation protocol as one of the major contributions, yet the changes are rather trivial. It only replaces COLMAP with GLOMAP, and fixing the GT camera parameters to the registration. 

All of the examples shown in the paper are in relatively small camera changes, which does not support the claims that CAMI2V can handle large camera movements with non-overlapped area. One of the example in Ours_I2V_on_unseen_image_3.gif also shows strong inconsistency across frames (5’th one from the left)

questions:
See weakness
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<thread>
creation date:
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thread_type:
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creator: reviewer_d8xb

<content>
summary:
The paper proposes a new method to inject camera pose conditions for controlled video generation using a diffusion model. The method uses Plücker coordinates to represent the camera rays and introduces epi-polar mask attention for cross-view attention, which is more efficient than the full 3D attention counterpart. The proposed method is evaluated on the RealEstate10K dataset and achieves state-of-the-art performance.

strengths:
+ The epi-polar mask attention layer proposed in the paper helps to enhance the camera control ability for video diffusion models. Also, it is plug-and-play – we don't need to retrain other modules of the original pretrained VDM.

+ It is an interesting idea to include the register token to handle cases where the epipolar constraint on correspondences fails, although more discussion and evaluation on it are needed (see the weakness section).

+ The paper addresses the inaccuracy in the SfM for robust evaluation metrics. Specifically, GLOMAP, a more state-of-the-art dense SfM pipeline, is used to validate the camera pose consistency.

weaknesses:
+ Discussion and experiment missing for a key statement in the paper: While the paper states (in L112) that register tokens are included to handle rapid camera movements, occlusions, and dynamic objects, this contribution (also the key difference from CamCo) is not discussed in more detail. For example, how does this additional token help to deal with the non-epipolar constrained correspondences? Can the image-level register token handle pixel-level dense correspondences across frames (like moving arms of people)? In addition, the register token is not ablated, so it’s unclear if it actually helps for dynamic scene generation.

Furthermore, since this is the key difference from CamCo, more discussion and evaluation could help to distinguish this submission from CamCo.

+ Some parts of the presentation are confusing: While the discussion in Fig. 1 and Sec. 1 on different types of conditions based on how much uncertainty the condition can reduce is interesting, how is that related to the epipolar condition? Does the epipolar condition reduce more uncertainty, hence making it a good condition? How does this view of condition serve as a principle to introduce the epipolar condition in the paper? More clarification is needed.

+ For the quantitative results, cross-view consistency is missing, although it has been highlighted in the qualitative subsection (Fig. 7). Is there any reason why it is ignored in the tables as a metric? Cross-frame consistency can be another useful metric to show how the epipolar attention helps, in addition to camera controllability.

+ In Fig. 5, the multi-resolution epipolar mask is shown but not discussed in the text. What is the motivation for using a multi-resolution epipolar mask?

questions:
Please refer to the weakness section above.
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<thread>
creation date:
1730722336924

thread_type:
official_review

<thread_object>
creator: reviewer_l7wx

<content>
summary:
This paper extends an existing I2V method (DynamiCrafter) to accept additional camera viewpoint control. The input cameras are parameterized as Plücker rays, serving as a position encoding. The authors additionally inset learnable Epipolar attention layers before temporal attn, which explicitly model cross-view geometry to further enhance the adherence to the input camera.

strengths:
The paper is well written with a few nicely created figures, e.g., Fig. 1 and Fig. 2. The ideas of (1) clean vs. noisy condition & (2) register tokens are neat (but they are also related to the weakness and questions below). Despite tuned with static/rigid scene dataset -- RealEstate10k, from the few generated videos in the supplementary, the motion dynamic of foreground is not lost too much. The discussion in L457-479 is good and clearly supports the design choices. For fair comparison, the authors spend effort reproducing the recipes of baselines (MotionCtrl and CameraCtrl) in their framework (instead of applying the released ckpt out of the box) such that they all stand on a common ground.

weaknesses:
* Contribution is clearly articulated in L.135-139 but not verified in the experiment: using Plücker embedding and/or epipolar attention has become a norm in the 3D generation literature, e.g. [1]. Applying one of them, if not both, in the video generation field has also been done, e.g., CameraCtrl, CamCo etc. Therefore, the biggest technical contribution I see in this work, to my knowledge, is applying the idea of register tokens to account for occlusion, zero epipolar scenarios, etc. Despite simple, I find this idea neat, but I don't see any experiments ablating this key idea to analyze the effect. A contribution/novelty has be verified by the experiments. If this concern can be addressed, I am happy to raise the rating.

* Method description is a bit insufficient. I need to read Sec. 5.3 to realize Plücker rays are also used as global positional encoding similar to CameraCtrl. This is also part of the final method so it has to be described in Section 3. 



[1] Kant et al., SPAD : Spatially Aware Multiview Diffusers, CVPR24. https://yashkant.github.io/spad/

questions:
1. The concept of clean vs. noisy conditions is also neat. The authors even make a figure to illustrate it (Fig. 1), but the knowledge of clean vs. noisy conditions seems not fully exploited in the method? For example, Fig. 1 points out that text and RT are clean conditions, but I don't see this new insight leads to new architectural designs? Adding camera information through Plücker embedding and epipolar attention is a natural choice due to the nature of multi-view geometry, not because it is a clean condition. I wonder if making such separation is really necessary in the exposition.  (This is more of a question for presentation, not technical details.) 

2. More generated video results. All video examples in the supplementary material show only zooming-out camera motion, which, in my experience, is the easiest one to be learnt by the network. Please provide other examples, such as panning left/right, moving up/down, etc.
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creator: authors

<content>
title:
To all reviewers: The Connection Between Our Insights on Conditions and the Proposed Method

comment:
We sincerely thank all reviewers and PCs for their valuable feedback and time. 
We are gratified that the reviewers appreciated the interest of our new insights regarding clean and noisy conditions, 
and how much the uncertainty reduced by a condition determines its quality. 
This affirms that the considerable effort we invested in presenting this viewpoint at the beginning of the paper was worthwhile. 
We apologize for not effectively connecting it with our proposed method. 
We will now clearly elucidate the connection between noisy condition and our proposed method. 
In addition, it’s strongly recommended to **check the visualizations in supplementary/index.html**.

# 1. The Connection Between Our Insights on Conditions and the Proposed Method
**Adding more conditions to generative models typically reduces uncertainty and improves generation quality**
(e.g. providing detailed text conditions through recaption). 
In this paper, we argue that it is also crucial to consider **noisy conditions** like latent features $z_t$, which contain valuable information along with random noise.
For instance, in SDEdit for image-to-image translation, random noise is added to the input $z_0$ to produce a noisy $z_t$. 
The clean component  $z_0$  preserves overall similarity, while the introduced noise leads to uncertainty, enabling diverse and varied generations. In this paper, we argue that **providing the model with more noisy conditions, especially at high noise levels, does not necessarily reduce more uncertainty, as the noise also introduces randomness and misleading**. This is the key insight we aim to convey.

To validate this point, we designed experiment with the following setups:

* **Plücker Embedding (Baseline, CameraCtrl)**: This setup has minimal noisy conditions on cross frames due to the inefficiency of the indirect cross-frame interaction (spatial+temporal attention).

* **Plücker Embedding + Epipolar Attention Only on Reference Frame**: Similar to **CamCo**, this setup treats the reference frame as the source view, enabling the target frame to refer to it. It accesses **a small amount** of noisy conditions on the reference frame. However, some pixels of the current frame may have no epipolar line interacted with reference frame, causing it to degenerate to a CameraCtrl-like model without epipolar attention.

* **Plücker Embedding + Epipolar Attention (Our CamI2V)**: This setup can impose epipolar constraints with all frames (including adjacent frames that have interactions in most cases to ensure an sufficient amount of noisy conditions).

*  **Plücker Embedding + 3D Full Attention**: This setup allows the model to directly interact with features of all other frames, accessing the most noisy conditions.

**The amount of accessible noisy conditions of the above four setups increase progressively**.  One might expect that 3D full attention, which accesses the most noisy conditions, would achieve the best performance.  However, as shown in the following **table**,  3D full attention performs only slightly better than CameraCtrl and is inferior to CamCo-like setup. Notably, our method achieves best result by interacting with more noisy conditions along the epipolar lines. It can also be clearly seen in **comparison in supplementary's index.html** that camco-like setup reference much on the first frame and cannot generate new objects. The 3D full attention generates objects within large movement due to its access to all frames pixels while it is affected by incorrect position of pixels.  These findings confirm our insight that **an optimal amount of noisy conditions leads to better uncertainty reduction, rather than merely increasing the quantity of noisy conditions.**

| Method                  | RotErr$\downarrow$ | TransErr$\downarrow$ | CamMC$\downarrow$ | FVD (VideoGPT)$\downarrow$ | FVD (StyleGAN)$\downarrow$ |
|-----------------|---------------|---------------|---------|---------|---------|
| DynamiCrafter            | 3.3415             | 9.8024       | 11.625   | 106.02      | 92.196     |
| +MotionCtrl          | 0.8636             | 2.5068         | 2.9536            | 70.820               | 60.363     |
| +Plucker Embedding (Baseline, CameraCtrl)    | 0.7098             | 1.8877               | 2.2557     | **66.077**        | 55.889      |
| +Plucker Embedding + Epipolar Attention Only on Reference Frame (CamCo-like) | 0.5738    | 1.6014               | 1.8851            | 66.439     | 56.778      |
| +Plucker Embedding + Epipolar Attention (Our CamI2V)    | **0.4758**         | **1.4955**      | **1.7153**        | 66.090       | **55.701**     |
| +Plucker Embedding + 3D Full Attention   | 0.6299             | 1.8215   | 2.1315     | 71.026  | 60.00     |
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creator: authors

<content>
title:
To All Reviewers: Key Differences Between CamCo and Ours

comment:
## 2. The motivation of this paper
With the extreme optimization of memory-efficient attention techniques, simply employing 3D full attention has already demonstrated remarkable 3D consistency in generated videos (e.g., OpenSora, CogVideoX). **This raises the question of whether merely scaling up models can address all challenges, while structural modifications might inadvertently degrade performance.**
Motivated by this, we **aimed to determine whether epipolar attention is effective in practice, especially in models under noisy settings like diffusion models, as opposed to its use in autoregressive models and pure transformers**. Our experiments support our novel insights on the role of noisy conditions in cross-frame interaction. Furthermore, we hope that sharing these new insights will provide readers with a more inspiring understanding and, by open-sourcing our work, enable more researchers to advance the development of this field.

## 3. Key Differences Between CamCo and CamI2V (Ours)

Firstly, we have great respect for the work of CamCo and have properly cited their contributions in our submission. While we acknowledge their significant advancements in the field, we would like to highlight several key differences between our approach and CamCo:

* **Application of Epipolar Attention Across Frames**: CamCo applies epipolar attention only to the reference frame (specifically, the first frame). In contrast, our method applies epipolar attention across all frames, ensuring a sufficient amount of noisy conditions and allowing the reference frame to be at an arbitrary position. This difference may stem from CamCo’s motivation, which originates from the concepts of “source view” and “target view” in the fields of 3D and multi-view generation to enhance geometric consistency. Our motivation, however, is based on the concept of \textit{noisy conditions**, leading to a different approach in handling cross-frame interactions.
    
* **Handling Missing Epipolar Intersections**: To the best of our knowledge, previous methods do not provide detailed explanations on how they handle situations where some pixels in the current frame have no intersecting epipolar lines in the reference frame. This issue arises not only in scenarios with large movements and pure rotations but also in common settings such as zooming out, panning, and rotating, where edge pixels disappear or new pixels appear. In 3D generation tasks, this problem is less pronounced due to simple camera movements where objects are typically centered, backgrounds are uniform, scenes are static, and the camera rotates around the object. In practical applications, the absence of epipolar lines is significant because it can lead to computational issues, such as NaN values resulting from zero-length key/value vectors in attention mechanisms. We address this problem by introducing a **register token**, which we discuss in detail later.
    
* **Implementation Differences**: CamCo proposes constructing epipolar lines by resampling visible pixel locations, avoiding variable-length epipolar line features, and improving the theoretical complexity from  $O((hw)^2)$  to  $O(hwl)$. However, we were unable to efficiently reproduce this approach and found it incompatible with current memory-efficient attention mechanisms, resulting in significantly increased memory usage and reduced training speed. This may be due to limitations in our reproduction, and CamCo’s code was not available. In our method, we implement epipolar attention using attention masks optimized for memory-efficient attention techniques, resulting in minimal increases in memory usage and computation time.

Our model with epipolar attention can be trained on GPUs with 24GB of memory and requires less than 12GB for inference at resolutions of 256×256 and 512×320. For a resolution of 1024×576, approximately 30GB and 18GB are required for training and inference, respectively. Detailed running times and memory usage are listed in the appendix. We emphasize the importance of training and inference efficiency for practical use, which is why we dedicate significant space in the method section to discuss our implementation of epipolar attention.
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creator: authors

<content>
title:
To All reviewers: Regarding the Register Token

comment:
## 4. Regarding the Register Token

Our contribution lies on the **consideration of situations without epipolar lines**. Our core design idea is to **avoid distinctions in implementation** to maximize the acceleration provided by memory-efficient attention. 
Therefore, we propose that **epipolar attention should still have placeholder tokens to query even when there are no tokens available.
This led us to introduce the register token**.

**We did not perform ablation studies on this component** because, **without the register token, directly applying epipolar attention based on attention masks can cause issues**. Specifically, when a pixel’s attention mask is all zeros, PyTorch’s memory-efficient attention implementation adds negative infinity to all entries, leading to **zero length of key/value tokens**. This results in **NaN** values during training, **making it impossible to train the model**. We apologize for not detailing this in the original manuscript. We are also unsure how other methods handle this situation, but it is quite common in camera-controlled image-to-video tasks, where pure rotations and large movements are common.

| Method                  | RotErr$\downarrow$ | TransErr$\downarrow$ | CamMC$\downarrow$ | FVD (VideoGPT)$\downarrow$ | FVD (StyleGAN)$\downarrow$ |
|-----------------|---------------|---------------|---------|---------|---------|
| DynamiCrafter            | 3.3415             | 9.8024       | 11.625   | 106.02      | 92.196     |
| +MotionCtrl          | 0.8636             | 2.5068         | 2.9536            | 70.820               | 60.363     |
| +Plucker Embedding (Baseline, CameraCtrl)    | 0.7098             | 1.8877               | 2.2557     | **66.077**        | 55.889      |
| +Plucker Embedding + register token + Epipolar Attention Only on Reference Frame (CamCo-like) | 0.5738    | 1.6014               | 1.8851            | 66.439     | 56.778      |
| +Plucker Embedding + register token + Epipolar Attention (Our CamI2V)    | **0.4758**         | **1.4955**      | **1.7153**        | 66.090       | **55.701**     |
| +Plucker Embedding + **no register token** + Epipolar Attention  | NaN         | NaN     | NaN       | NaN       | NaN    |

We categorize two cases where epipolar line do not exist:

* **Degeneration into Epipolar Planes**: During pure rotation, the epipolar line degenerates into an epipolar plane. In this scenario, the pixel does not disappear but rotates to another position in the image. By searching all pixels, we can locate the pre-rotation pixel, effectively making the attention mask all ones. In this case, the epipolar line is defined as $ax+by=0, a=0, b=0$ and becomes a plane, thus the distance from each pixel to this plane becomes zero. All the zero distance pass through our predefined threshold, making a full mask. This situation then becomes equivalent to full attention on that frame.
    
* **Epipolar Lines Falling Outside the Image**: Large movements can cause the epipolar line to fall outside the image area—the line exists, has no intersections with the region of image and the distance is too distant. In this case, using only two register tokens avoids the zero attention issue and ensures the succesfull calculation on attention scores. Despite utilizing only two register tokens is insufficient, it ensures the model's basic functionality and only part of pixels on the current frame who have no epipolar line degrade to CameraCtrl without epipolar attention (it can also be considered as a kind of epipolar attention as in this situation, it is appropriate to aggregate no features under epipolar constraint).

**Initially, we believed that the core contribution of the paper was the concept of noisy conditions rather than epipolar attention, which has been applied multiple times before.** Therefore, we did not provide detailed explanations, which is our fault. 

## 5. Citation Updates Based on Reviewers’ Feedback
We will carefully review our citations in light of the reviewers’ comments 
and update the Related Work section accordingly. Specifically, 
we will include the papers mentioned by the reviewers 
that utilize Plücker embeddings and epipolar attention in the fields of 3D generation 
and multi-view synthesis. 
If there are any additional references we may have missed, please feel free to inform us.

## 6. Improvement On Camera Controllability without FVD Decrease
Our method demonstrates significant improvements over CameraCtrl, achieving a **32.96% reduction in Rotation Error**, a **25.64% decrease in CamMC**, and a **20.77% improvement in Translation Error**, **without decrease in FVD**. These results were obtained using Text and Image CFG set to 7.5, 25 steps, and **camera CFG set to 1.0 (no camera cfg)**. 

Compared with CamCO-like (arxiv in June) method, we improve **17.08%, 6.61%, 9.00%** on RotErr, TransErr, and CamMC without FVD decrease, respectively.
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creation date:
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creator: authors

<content>
title:
Final Reminder and Request for Further Feedback

comment:
We sincerely thank all reviewers for their valuable feedback and constructive discussions throughout the review process. As a friendly reminder, **only one day remains for submitting your responses**. This notice is prompted by the fact that some reviewers have not yet submitted their rebuttal responses, and others may still have unresolved questions or concerns.

We have worked diligently to address the majority of reviewers' concerns regarding **the connection between the novel insights and the proposed methods**. Specifically, we have clarified key points such as **the relationship between a condition and its ability to reduce uncertainty**, and identified one of the critical challenges in the field of camera-controlled image-to-video generation: **modeling cross-frame interactions under noisy settings**. Additionally, we **introduced the perspective of noisy/clean conditions to explain why applying epipolar constraints across all frames is essential**.

**We look forward to your continued support and hope you will consider raising your scores** to reflect the improvements made and the contributions of our work. If you have any further questions or concerns, please feel free to reach out—we remain available to address them.

Thank you again for your time and thoughtful feedback.
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Metareview:
metareview:
This paper presents an image-to-video diffusion model that enables camera control. The input cameras are represented as Plücker ray embeddings. Epipolar attention is used to aggregate cross-frame features and enhance cross-view geometry. The paper introduces register tokens as placeholders to avoid NaN in cases without intersections between frames.

While the proposed method has some acknowledged merits, reviewers have expressed concerns about its novelty and view its contributions as incremental. The area chair has also reviewed the paper and shares similar reservations. Despite the authors' efforts to clarify key contributions in their rebuttal and revision, the concerns remain. Consequently, the paper received final ratings of 5, 5, 6, and 6, which indicates that it does not have enough support for acceptance.