In this work, we present MotionBooth, an innovative framework designed for animating customized subjects with precise control over both object and camera movements. By leveraging a few images of a specific object, we efficiently fine-tune a text-to-video model to capture the object's shape and attributes accurately. Our approach presents subject region loss and video preservation loss to enhance the subject's learning performance, along with a subject token cross-attention loss to integrate the customized subject with motion control signals. Additionally, we propose training-free techniques for managing subject and camera motions during inference. In particular, we utilize cross-attention map manipulation to govern subject motion and introduce a novel latent shift module for camera movement control as well. MotionBooth excels in preserving the appearance of subjects while simultaneously controlling the motions in generated videos. Extensive quantitative and qualitative evaluations demonstrate the superiority and effectiveness of our method. Our project page is at https://jianzongwu.github.io/projects/motionbooth
Paper
Similar papers
Peer review
Summary
The authors propose MotionBooth, a method to fine-tune a pre-trained text-to-video model on a collection of images of a specific object to enable the model to generate controlled videos of that object. Fine-tuning incorporates three losses: diffusion loss on image data, restricted to the object's region; video preservation loss, i.e diffusion loss on sample video data, to prevent overfitting to static images; subject token cross-attention loss, to improve controllability at generation time. During inference the motion of the object is controlled via editing the cross-attention maps to amplify the attention to the object tokens within the object's bounding boxes. The camera is controlled via shifting the noised latent and filling the new regions with latents sampled from the background of the original noised latent.
Strengths
The problem of controllable video generation is of high significance nowadays. The method proposed in the paper is relatively simple and the demonstrated visual results confirm the improved controllability of MotionBooth over prior work. The contributions in the paper are supported with well-designed figures aiding the clarity of the presentation. The limitations of the proposed method are discussed in the appendix.
Weaknesses
1) **Novelty**: - Similar training-free object motion control was proposed in [1] and [2] for image and video models respectively, where cross-attention amplification within object's bounding box was used to control its location in the generated frame. Similar training-free camera motion control was used in [3], where a series of noised latent shifts were used to control the global scene and camera motion. Discussion and comparisons with those works are missing in the paper. - Some missing references: [4, 5]. 2) **Method**: - Based on the formula for subject motion control, the attention is suppressed (set to -infinity) for all query-key pairs other than the queries from the bounding box and the keys from the object tokens. This way it seems that the rest of the prompt is ignored. Moreover, it is stated in the paper that "the attention suppression outside the bounding box regions persists throughout the generation". Details clarifying this are missing in the paper. 3) **Evaluation**: - The paper lacks quantitative evaluation. Comparing CLIP features between the frames doesn't measure temporal consistency. Metrics like FVD, or optical flow warpping error could be better choices for this purpose. - Details on how exactly the metrics are calculated are missing. Formal definitions of newly introduced metrics are required for better understanding. E.g. how exactly is the flow error calculated in the case when not all the motion except for the object's motion corresponds to camera motion? Having those would help with understanding the significance of the improvements reported in the tables. - The evaluation dataset consists of limited number of different object classes, mostly dogs, cats and toys. Evaluation on more general collection of object classes would better support the claims made in the paper. - More ablation studies would better illustrate the contributions. E.g. what would happen if the missing region in the shifted noised latent was not filled with samples from the same noised latent? Or if camera control like in [3] was used instead? 4) **Results**: - Figure 14 shows that bounding boxes often don't fully contain the object of interest. Given that the attention is suppressed outside of bounding boxes, this looks like a flaw in the proposed control and needs to be investigated. [1] Ma et al., Directed diffusion: Direct control of object placement through attention guidance, AAAI 2024. [2] Ma et al., TrailBlazer: Trajectory Control for Diffusion-Based Video Generation, arxiv 2024. [3] Khachatryan et al., Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators, ICCV 2023. [4] Wang et al., Boximator: Generating rich and controllable motions for video synthesis, arxiv 2024. [5] Chen et al. Motion-Zero: Zero-Shot Moving Object Control Framework for Diffusion-Based Video Generation, arxiv 2024.
Questions
1) Could the authors discuss the connection between the proposed MotionBooth and the prior work with similar training-free controls? Some ablation studies would be ideal. 2) Could the authors provide more details about their subject motion control attention suppression technique? Especially regarding the issue with attention to the prompt tokens other than the subject language tokens. 3) The authors claim that their fine-tuning is efficient (line 3 in the abstract). Could the authors provide more details on this? E.g. what is the training time to adapt the model to a new subject?
Rating
3
Confidence
5
Soundness
2
Presentation
3
Contribution
2
Limitations
The authors have adequately addressed the limitation and the potential negative societal impact of their work.
Summary
The paper proposes a video generated method - for customizing a subject, along with its motion, provided in the form of bounding boxes, and camera movement provided in the form of camera pose. To do this, the training method has a subject region loss to prevent bias from background and other details in the image, and a video regularization loss function to ensure that video generation properties are not forgotten. At inference, in order to control camera movements, latents are shifted. Cross-attention maps are edited with the provided bounding boxes, to fix the subject motion.
Strengths
The paper is written well. I particularly like the introduction and related work, where the paper clearly motivates the problem, describes the challenges and goes on to describe how it solves the problem. The method comprises of various components, and each of the components is designed very well - they are intuitive, make sense and well formulated. The method very systematically addresses various challenges involved in the problem by designing various relevant techniques. The paper is also written very well to motivate and describe the method.
Weaknesses
My main concerns are with the experiments, which are very non-convincing. While the paper does provide detailed quantitative results for comparisons and ablations on various metrics validating the solution, the qualitative results do not support that. 1. I watched the supplementary video, which has the videos for the results presented in the paper. First of all, the number of qualitative results provided is severely limited, raising questions about cherry picking. Second, the method seems to perform reasonably well on cases that either have camera motion or subject motion - the results look good. In cases involving both camera as well as subject motion, which is the theme of the paper, the results are highly suboptimal - for instance, at 1:48 of the video, the last video showing the dog does not show the subject motion appropriately. 2. I would like to see more detailed results that segregate the various kinds of motion (camera and subject), to see how effective the method is for just camera motion, just subject motion and a combination of both. This will give a clearer picture of where the method is failing.
Questions
I am unable to judge the quality of this work with the limited number of qualitative results that have been provided. Moreover, the provided qualitative results are sub-optimal, esp in the case of subject + camera motion, questioning the working of the method. Given that the limit on the size of the supplementary material is considerably large, it would have been nice if the authors had provided more results showcasing their method. Since the rebuttal does not allow for more qualitative results, I am rejecting the paper at this stage. The method, while pretty good, probably needs a little more work to get it working for the subject + camera motion case. I recommend that the authors continue working on it to strengthen the paper. Post rebuttal: it would have been nice to see more qualitative results and videos, but i understand that is not possible in the 1 page rebuttal pdf. Based on the submitted rebuttal, I am happy to increase the score.
Rating
6
Confidence
5
Soundness
3
Presentation
3
Contribution
3
Limitations
Yes
Summary
This paper addresses the challenge of how to control the identity and the motion of the subject while generating video, in a text-to-video setup. Specifically, in addition to the standard text prompt, it allows the user to control: the subject's identity by providing a few images of it (e.g. 5 photos of my dog); the subject's position in the video, as provided by a bounding box sequence; and the camera motion, as provided by a sequence of delta-x & delta-y. Identity preservation is achieved by two changes to the loss function, during fine-tuning on images of the subject. First a "subject region loss" zeros out gradients that lie outside the subject's bounding box during fine-tuning, and second a video preservation loss (similar to the class-specific preservation loss introduced by DreamBooth) to ensures that the ability to generate dynamic motion is not lost due to over-fitting on a few static images. Subject and camera motion are controlled at inference time, by modifying the cross-attentional map between the prompt and the latent feature, by shifting the latent feature, respectively. Finally, a third loss "subject token cross-attention" links the two, enabling the inference motion controls by ensuring that the cross-attention between the subject token (in the text prompt) and the diffusion model's U-Net features is strong where the object should be and weak elsewhere. These proposed changes are validated quantitatively through several metrics: CLIP and DINO scores between the source images and the desired video locations of the subject, CLIP score between the text prompt and video frames, and a difference in optical flow between the generated video and ideal video (according to the subject and camera motion prompts). Examples of generated videos are included in the supplemental.
Strengths
This paper addresses a very significant and timely problem -- how to effect significantly more fine-grained control in text to video models, and allow them to be more than a novelty. The results, as shown in the supplemental video, are very impressive: the subject identity, subject motion, and camera motions very much follow the user's input. The additional loss terms are intuitively sensible and effective. The inference modifications, although they seem a bit "hacky" (if you'll excuse me), do get the job done quite effectively. I appreciate that the transferability of the method was demonstrated by implementing it on two different text-to-video models.
Weaknesses
These are relatively minor weaknesses in my opinion: This region metrics measure whether the desired object is translating around the frame as per the bounding box guidance, but because they are image metrics, can't evaluate the actual "animation quality" of the subject. I believe the entire suite of metrics could be optimized by a static image crop of the subject sliding across the video frame according to bounding box motion and flow. Can you confirm whether this is correct? The set of camera motions that can be models is limited to 2d translations in x and y, missing zooms, rotations, etc. Although this paper presents its techniques as transferable across many latent diffusion models, they require that the "latent can be considered a "shrunk image," which maintains the same visual geographic distribution as the generated images." This should generally be true for most latent video diffusion models, but may not work with new latent encoders being developed.
Questions
Re "Subject Region Loss": How certain are you that the binary mask, when applied to the latent (rather than the pixels), actually does mask out the background? Given that the latent is produced by a stack of many convolutional or transformer layers, the receptive fields of each latent location almost certainly include information from the nearby background. Have you tried masking the training images directly in pixel space, rather than masking the diffusion loss? How does that compare? Re "Ablation studies": w/o video and w/ class video give quite-nearly identical numbers. Is this a copy-paste error? If not, it's worth discussing in more detail. In Figure 3, what is the difference between c, d, e, and f? Are they different samples from the model, or from different models? Have you tried to achieve the desired camera motions via the text prompt, and considered that as a baseline? I have seen it work well for many basic types of motions in some recent t2v models that you cite.
Rating
7
Confidence
4
Soundness
3
Presentation
4
Contribution
3
Limitations
The limitations described in the appendix are important enough that they should be included in the main paper. Otherwise, yes, they have addressed limitations and impact.
Response to Reviewer N7np
Dear reviewer: Thanks for your comments. We will update our draft following your comments with the analysis on camera zoom in/out, text-based camera control, and the exploration of masking in pixel space. Best regards! Authors of #9629.
Summary
This paper primarily addresses the problem of motion-aware customized video generation. Traditional customized generation methods either tend to lose the motion information of the video or require additional training of motion modules. To enhance subject fidelity and video dynamics, this paper introduces subject region loss and video preservation loss. Additionally, a training-free subject- and camera-motion control method is proposed, enabling the model to flexibly control the motion information of the video. Extensive experiments demonstrate that the proposed method outperforms existing state-of-the-art methods.
Strengths
1. This paper achieves better automized generation results than the state-of-the-method 2. The writing is clear and easy to follow
Weaknesses
1. Some details need to be added to more clearly distinguish the differences. For example, in line 169, existing methods use class-specific data, whereas this paper uses common videos. Do the common videos need to include customized subjects? How are these common videos obtained? 2. The assumptions made by the method, such as sampling operations on the x- and y-axes when controlling camera motion, may limit its effectiveness. For instance, in scenarios with significant changes from left to right or top to bottom, or in close-up videos with large foreground objects, this operation may result in uncoordinated outcomes. However, the paper does not seem to discuss these limitations. 3. Methods that control subject and camera motion through object trajectories and camera parameters have also been proposed, such as motionCtrl, TrailBlazer, Boximator, and motion-zero. However, this paper does not provide an in-depth discussion or experimental comparison with these methods. Although some of these methods are training-based, it is still worthwhile to explore the existing gaps.
Questions
1. How many subject samples and/or common videos are used for training each customized model? 2. The proposed mainly explores the camera motion on panning left or right, is it possible to control other motion types, like zoom-in/out effects? 3. what is the difference between the proposed subject region loss, subject token cross attention loss, and those losses in Mix-of-show [1] for customized image generation ref: [1] Mix-of-Show: Decentralized Low-Rank Adaptation for Multi-Concept Customization of Diffusion Models
Rating
5
Confidence
4
Soundness
3
Presentation
3
Contribution
3
Limitations
1. This paper needs to discuss more methods addressing the same tasks, like motion control, and specify the differences between the proposed loss functions and those of existing methods. 2. More limitations or failure cases needed to be discussed.
Please let us know whether we address all the issues
Dear reviewer, Thank you for the comments on our paper. We have submitted the response to your comments and a PDF file. Please let us know if you have additional questions so that we can address them during the discussion period. We hope that you can consider the raising score after we address all the issues. If you still have more questions and concerns, please comment here. We will reply it as soon as possible. Thank you
I thank the authors for their thorough rebuttal to my and other reviewers' questions. Your answers helped me better understand your method and clarified several of my concerns. However, some of my main concerns remain. I was pleasantly surprised to see that the zoom in/zoom out effect could be achieved by enlarging or reducing the bounding box. However, this effect is not implemented by the camera movement control module, which diminishes the contribution of the camera movement control. Other methods that do not rely on bounding boxes might not be able to achieve this effect. Additionally, I feel that this approach may also struggle to achieve camera motion control that involves rotating around an object. Additionally, even though the Mix-of-Show paper does not mention it, its code uses an attention regularization loss that is quite similar to the subject token cross-attention loss in this paper. It would be helpful if the paper could also explore the effects and differences between these two implementations. Of course, since the Mix-of-Show paper does not explicitly state this, it will not be a factor in my scoring. In summary, I appreciate the completeness of the paper, but the innovation seems somewhat limited to me. Nonetheless, I still hold a positive view toward the paper's acceptance, so I will maintain my score.
Response to Reviewer ZPXz
Dear Reviewer, Thank you for your valuable feedback and for taking the time to review our responses. We appreciate your insightful comments, which have helped us further refine our work. We will update our manuscript to reflect the functional limitations of the camera motion control technique, particularly highlighting that the zoom in/zoom out effect can indeed be achieved by adjusting the size of the bounding box. We acknowledge that while this method is effective, it does not utilize the camera movement control module, which might reduce the perceived contribution of that component. Additionally, we understand your concern that other methods not reliant on bounding boxes might struggle to replicate this effect, and we also recognize the challenges in achieving camera motion control that involves rotating around an object using our current approach. Regarding your comments on the comparison of losses with the Mix-of-Show code, we have explored this further and would like to provide additional clarification. The code url is at `https://github.com/TencentARC/Mix-of-Show/blob/main/mixofshow/pipelines/trainer_edlora.py`. 1. We have identified that the Mix-of-Show code includes a mask loss and subject token loss, which are used during the training of ED-LoRA. The mask loss, implemented in line 252 of the code, is indeed the same as ours. Both approaches focus the learning process on the subject region, minimizing the influence of background elements in the training data. This similarity is likely due to the shared goal of enhancing subject representation. 2. The subject token loss in Mix-of-Show is implemented between lines 254-259 and further detailed in lines 263-313. This loss is composed of two parts: subject token loss and adjective token loss. Both are MSE losses that penalize attention outside the subject mask while enhancing attention within the subject mask. While similar to our approach, there are notable differences: - Mix-of-Show calculates both subject and adjective token losses, whereas our method focuses solely on the subject token loss, allowing for a more concentrated emphasis on the subject itself. - Importantly, in Mix-of-Show, this token loss is disabled by default (as indicated in line 29 of the code). Additionally, the training configuration provided in their `README.md` also disables this loss. Given that this loss is not explicitly mentioned in their paper, it appears that it may have been an ablation experiment rather than a core component of their main experiments. - Mix-of-Show includes a `reg_full_identity` argument in their training configuration, which is also disabled by default. When disabled, the loss formula is $\mathcal{L}_1 = - \mathbf{M} \log(\mathbf{A})$, while when enabled, it becomes $\mathcal{L}_2 = - \left[\mathbf{M} \log(\mathbf{A}) + (1 - \mathbf{M}) \log(1 - \mathbf{A}) \right]$. Only the latter formula aligns with the STCA loss in MotionBooth. - Furthermore, the comment in line 30 of the Mix-of-Show code suggests that the choice of loss formula may vary depending on the subject (e.g., "Thanos" vs. a real person), while our approach consistently applies the loss across all our experiments. We hope this detailed explanation addresses your concerns. We will incorporate these comparisons and clarifications into our paper to provide a comprehensive discussion of the similarities and differences between our approach and that of Mix-of-Show. Thank you once again for your thoughtful review. Best regards, Authors of #9629
Summary
This paper targets at animating customized subjects with both motion and camera control. To achieve that, they first customize t2v models with subject images finetuning without hurting video generation capability. Then, they propose a training-free approach to manage subject and camera motions. Extensive experiments demonstrate the superiority of their method.
Strengths
• The paper introduces the first unified video generation framework for subject customization with motion and camera movement control, which is also an interesting problem setup. • The paper proposes a new subject region loss and video preservation loss for video customization, and a training-free method to control both subject motion and camera motion. • The paper is well-written with clear motivations.
Weaknesses
• The subject motion control module is interesting by directly amplifying in the bounding box region and suppressing outside the box region. However, how to define the value of parameter alpha remains unclear. Is it only defined based on experimental performance? And does the authors keep this alpha parameter the same for all customization experiments? • The proposed method only control the camera movement in 2D such as up, down. However, camera movement is quite important for 3D, such as moving into the scene, rotation, etc. The proposed camera control module is not able to handle such camera motions and can be a weakness for camera control.
Questions
• For subject motion control module, if the bounding box size is changing, e.g., gradually enlarging bounding box, will the method enlarge the subject accordingly? • Subject motion is composed of both position translation and subject movements, such as walking, dancing, jumping, etc. The paper only control the subject position for subject motion. The reviewer wonders is it a bit overclaiming for controlling subject motions, since the subject movements cannot be customized or controlled?
Rating
5
Confidence
4
Soundness
3
Presentation
3
Contribution
3
Limitations
Yes.
Please let us know whether we address all the issues
Dear reviewer, Thank you for the comments on our paper. We have submitted the response to your comments and also more results are shown in PDF file. Please let us know if you have additional questions so that we can address them during the discussion period. We hope that you can consider raising the score after we address all the issues. Thank you
Response by Reviewer aAio
Dear Authors, Thanks for your detailed responses. I have read through the rebuttal and the rebuttal has solved most of my concerns. Enlarging and shirking subjects performance is good. However, the reviewer thinks the method is a bit limited to coarse camera control and subject position control, and cannot control subject motions such as walking, jumping, etc. Therefore, the reviewer decides to keep the original score. Best regards, Reviewer aAio
Response to Reviewer aAio
Dear reviewer: Thanks for your comments. We will update our draft following your comments with the analysis on camera zoom in/out, the limitations of the camera, and the subject motion control ability. Best regards! Authors of #9629.
Response to rebuttal
Dear authors, Thanks for your very detailed rebuttal. I appreciate the experiments segregating the various types of motion and the additional qualitative results. They look good! It would have been nice to see videos and more qualitative results, which are highly limited, but I understand that this is not possible at the post submission stage in a 1 page rebuttal. I am happy to increase my score based on the rebuttal. Thanks, Reviewer
Response to Reviewer yPkE
Dear reviewer: Thanks for your comments and increasing the score for our work. We will update our draft following your comments with more qualitative results and comparison. Moreover, we believe our method can also be applied with stronger text-to-video base models. We will add these later once the base models are open-sourced. Best regards! Authors of #9629.
Thank you for including the many clarifications, and the additional analyses. In particular, I appreciate the examples demonstrating camera zoom in/out, text-based camera control, and the exploration of masking in pixel space. I have read through the other review and authors responses, and believe all of my questions and concerns have been addressed.
Response to the rebuttal
Dear authors, Thank you for your detailed rebuttal. However, I still have some concerns and would keep the same rating for now. 1) Could you clarify how the comparisons in table R.1 (f) are done? E.g. are the backbones the same? Is the subject learning performed for all the variants? 2) Do you have any explanation why the reflect method collapsed in your case, while it seems to work reasonably well in the Text2Video-Zero paper? 3) Regarding the suppression formula. So is the equation 7 in the paper incorrect? In the rebuttal you are saying that all the tokens are suppressed to $-\infty$ for the later denoising steps, while in the equation 7 the attention edit for the subject tokens is 0. Note that if all the tokens are suppressed to $-\infty$ then the attention is indeed equivalent to taking the average of all the values, while in the case when there are any finite pre-softmax values in the attention matrix, only those will be weighted despite the normalization. Best regards, Reviewer
Response to reviewer q2XJ
Dear reviewer: Thanks for your comments. Here are our reply for your questions. # Details about the expriments in `Table R.1 (f)` Thank you for your question. In Table R.1 (f), all the baseline experiments are conducted using the Zeroscope base model for consistency across the experiments. Additionally, the subject learning process of MotionBooth is applied uniformly to all the baseline models to ensure a fair and direct comparison. # The reason why the reflect method fails Thank you for your question. The discrepancy between our results and those reported in the Text2Video-Zero paper can be attributed to the difference in how the initial latent $z_T$ is handled in the two approaches. In the Text2Video-Zero paper, the authors utilize the same initial random latent $z_T$ for all frames of the video, which is then denoised with a few steps to $z_t$. The reflect latent shift is applied to $z_t$, and noise is added back to generate $\hat{z}_T,$ which serves as the starting latent for denoising the video frames. This method works well within their framework because they extend a pre-trained text-to-image (T2I) model for video generation, allowing them to maintain consistency across frames by using the same $z_T$ throughout the process. However, our model is designed for text-to-video (T2V) generation, where the initial latent $z_T$ is randomly initialized for each frame. This approach is crucial for capturing the frame-by-frame variations necessary in video generation. When we applied the reflect method under these conditions—where $z_T$ is distinct for each frame—it resulted in collapsed outputs. This suggests that the reflect latent shift method struggles when applied to distinct latents for each frame, which is a key requirement in our T2V model. Furthermore, we also tested using the same latent initialization ($z_T$) across all frames in our model, similar to the method used in Text2Video-Zero. Unfortunately, this approach also led to collapsed results, even when there were no subject or camera motion controls in place. This is because the pre-trained T2V model requires distinct initialized latent for each frame. This reinforces our conclusion that the reflect method is not suitable for models that require distinct latents for each frame, as is the case in our T2V framework. We hope this explanation clarifies why the reflect method did not work in our scenario despite its effectiveness in the Text2Video-Zero paper. # Re-clarification about the suppression formula Thank you for your insightful question and for giving us the opportunity to clarify our approach. We apologize for any confusion caused by our previous explanation. After thoroughly reviewing our code and methodology, we confirm that Equation 7 in the paper is correct. To clarify, our motion control module performs suppression across all denoising steps, but this suppression is only applied outside the subject's bounding box region. Within the subject region, amplification is applied during the earlier steps, while in the later steps, there is neither amplification nor suppression within this region. Regarding the suppression of tokens, the language tokens, except for the subject tokens (e.g., verbs, background nouns), are indeed effectively ignored across the denoising process because they are assigned $-\infty$ values. This stark difference in token values is intentional, as it allows us to strongly amplify the motion control of the subject. This raises the question of how the necessary information from other language tokens is incorporated into the generated results. We believe that this information is retrieved through the <start> and <end> tokens. As mentioned in lines 216-217 of the paper, we do not perform any attention score editing for these two tokens. This means they can share the softmax outputs from the subject tokens since they both have finite values. Transformer-based language encoders, such as CLIP or BERT, tend to encode the overall information of a sentence into these special tokens, given their training with classification tasks. As a result, even though other tokens are ignored in the softmax function, the model can still retrieve the necessary information about verbs, backgrounds, and other elements for the generated results. To support this claim, we conducted a toy experiment where we printed out the softmax outputs in a vanilla text-to-video pipeline using the Zeroscope model. We observed that the softmax value of the <start> token is the highest, nearing 1, while the <end> token has the second-largest value. The remaining tokens, including nouns, adjectives, verbs, and conjunctions, share the remaining softmax values. We hope this explanation addresses your concerns, and we are happy to provide further clarification if needed. Best regards! Authors of #9629.
Please let us know whether we address all the issues
Dear reviewer, Thank you for the comments on our paper. We have submitted the response to your comments. Please let us know if you have additional questions so that we can address them during the discussion period. We hope that you can consider raising the score after we address all the issues. Thank you
Response to the authors
Dear authors, Thank you for additional clarifications, most of my concerns were carefully addressed in the rebuttal and the comments. However, unfortunately, the discussion about the suppression formula has left the impression that the authors did not fully understand how and why their method worked, and hence I have to keep my original rating. Best regards, Reviewer
Response to Reviewer q2XJ
Dear Reviewer, Thank you for your thoughtful engagement with our work and for acknowledging that most of your concerns were carefully addressed in our rebuttal. We sincerely appreciate the time and effort you have invested in reviewing our submission. We understand that the discussion surrounding the "token suppression" formula may have left some concerns unresolved, and we deeply regret that our initial response did not fully clarify this aspect of our method. With numerous questions from five different reviewers, we recognize that our initial explanation may have been unclear, and we apologize for any confusion this may have caused. Once you highlighted the misunderstanding in your follow-up comments, we took immediate action to revisit the issue. We carefully re-examined our paper, code, and rebuttal. We are sure that we have made a correct and accurate explanation, which aligns with the method as described in our work. Our intention was to clarify any ambiguities and ensure that our method was clearly understood. We respect your decision to maintain your original rating, but we hope you will consider that the misunderstanding was promptly addressed during the discussion phase. We believe that our work's overall contributions, including the introduction of a novel framework for motion-aware video generation and innovative techniques for subject and camera motion control, should be taken into account in the final evaluation. We value your feedback immensely and have learned from this experience. We sincerely hope that our clarification, combined with the broader contributions of our paper, will be considered in your final assessment. Thank you once again for your time, patience, and thoughtful consideration. Sincerely, Authors of #9629
Decision
Accept (spotlight)