Title: E-Motion: Future Motion Simulation via Event Sequence Diffusion
Venue: NeurIPS 2024
Accepted: yes
Keywords: Event-based vision

video diffusion model
Abstract: Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capture changes in the scene with exceptional temporal granularity, may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. Inspired by that, we propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Specifically, we initially employ pre-trained stable video diffusion models to adapt the event sequence dataset. This process facilitates the transfer of extensive knowledge from RGB videos to an event-centric domain. Moreover, we introduce an alignment mechanism that utilizes reinforcement learning techniques to enhance the reverse generation trajectory of the diffusion model, ensuring improved performance and accuracy. Through extensive testing and validation, we demonstrate the effectiveness of our method in various complex scenarios, showcasing its potential to revolutionize motion flow prediction in computer vision applications such as autonomous vehicle guidance, robotic navigation, and interactive media. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems. The source code is
publicly available at https://github.com/p4r4mount/E-Motion.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes a novel approach to integrate event-sequences with a video diffusion model for event-based future motion prediction. The authors integrate the learning capacity of video diffusion models with the rich motion information of event cameras to create a motion simulation framework and propose to align the event-sequence diffusion model with the real-world motion via a reinforcement learning process. They demonstrate the effectiveness of their method in various scenarios and highlight its potential in several downstream applications.\n\nSTRENGTHS:\n(1) The paper makes the first attempt to combines event-based sensors with video diffusion models, offering a unique solution for future motion prediction.\n(2) The paper provides a unique solution to align the pre-trained event-sequence diffusion model with the real-world motion via a reinforcement learning process.\n(3) The paper includes sufficient testing and validation, demonstrating the effectiveness an potential of the proposed method.\n\nWEAKNESSES:\n(1)\tThis paper lacks an deep analysis of the relationship between event cameras and future motion estimation tasks. If only the role of high temporal resolution is emphasized, high-speed cameras are also an alternative, of which the spatial content is richer.\n(2)\tThe proposed solution leans towards image-based techniques and lacks to utilize the characteristics of events.\n(3)\tThe rationale for incorporating reinforcement learning remains unclear to me. I hope the authors can provide a more convincing justification.\n(4)\tSome experiment settings lack explanations. For example, in Table 2, the specific processes corresponding to 'T' and 'S+T' are not clearly described. Although I can infer from the supplementary materials that they correspond to spatial and temporal attention layers, this is difficult for readers to understand in main text.\n\nQUESTIONS:\nSee Weaknesses.\n\nLIMITATIONS:\nNo", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-07"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper explores video diffusion models on the modality of information captured by event cameras. Stable Video Diffusion model is fine-tuned on an event stream dataset. On top of traditional diffusion set up, additional training using FVD and SSIM losses as rewards in a PPO is done. A method to inject motion priors during inferences further proposed. The method is evaluated in terms of generation quality downstream segmentation and object tracking applications.\n\nSTRENGTHS:\n1) The paper explores an interesting problem of predicting future motion based on temporally dense settings offered by event cameras.\n2) The proposal builds on successes of SVD models and adapts to work in  the event stream domain. Additional techniques such as PPO-based optimisation and guided sampling are also interesting.\n3) The writing is sufficient, and the presentation of the results is good.\n\nWEAKNESSES:\n1) While the main motivating point is to benefit from the specifics of event stream data, the processing of it is done by treating it mostly like RGB stream, including VAE and CLIP encoders (B.1 Fig. 5). Does this not abandon the benefits of the event stream data?\n2) The majority of the metrics used are defined and proposed in the RGB space (FID, SSIM, LPIPS). However, they appear to be applied on top of the event stream data. It is not immediately clear whether this is correct or would signal the results in the same way. Moreover, the results appear to be presented alongside the evaluation done on RGB modality, although these are not directly comparable. \n3) The main motivation is \"future motion estimation\"; however, the predictive accuracy is measured in a more perceptual, structural way (SSIM, LPIPS) and not on more \"raw\" metrics like PSNR or MSE.\n\nQUESTIONS:\nThe main point to address in the rebuttal is the discrepancy between the commonly RGB-based metrics like FID, SSIM, LPIPS, and whether it makes sense to apply them to event data.\n\nLIMITATIONS:\nThe limitations are discussed.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "SOUNDNESS_CORRECTNESS": 1, "DATE": "2024-07-10"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work focuses on the task of future motion estimation, where the goal is to leverage event-based vision sensors (an alternate modality, compared to traditional vanilla RGB inputs) to predict motion flow in settings useful for robotics and autonomous vehicles. The authors propose a method that leverages stable video diffusion models (pretrained on RGB settings) and adapting them with an event sequence dataset for this specific task. They authors consider two large-scale datasets (VisEvent and EventVOT), showing improvements in FVD and mIoU compared with prior work, and extensions to settings like video object tracking. The paper also discusses ablations of the method, with varying prompts and fine-tuning techniques.\n\nSTRENGTHS:\n`+` Motion estimation (both flow and video object tracking) are important sub-tasks for embodied vision applications (robotics, autonomous vehicles).\n\n`+` The proposed framework is a sensible extension on traditional RGB-only stable diffusion models, and represents a good early exploration of incorporating recent techniques to this relatively smaller focused area of research.\n\n`+` The results indicate promising improvements over ablation variations and key baselines for this task, and the authors examine several different downstream tasks (segmentation, tracking and flow estimation).\n\nWEAKNESSES:\n`-` *Additional analysis w.r.t. baselines.* It is unclear why some of the metrics show regression in the ablation analysis and comparison tables. For example, in Table 1, TAU and SimVP outperform on FID and aIoU metrics, while the qualitative visuals seem to indicate a substantially different picture (are there examples for which these prior work show more compelling visual results, and if so, what areas of improvement could be identified by this?). Relatedly, another example is in Table 2/3, where it is unclear why some ablations (e.g., removing motion alignment with RL, one of the core technical listed contributions) show improvements over the full approach. If the authors could expand further on this analysis it would be helpful to understanding the overall value and impact of the work relative to the prior work in this space.\n\n`-` *Novelty beyond specific context of event sequence inputs.* This specific area for video event understanding is relatively niche, so while novelty is present, it is also limited to this specific context. In particular, the broader ideas around incorporating similar signals for video diffusion (inputs and outputs) have been explored previously, e.g. [A1, A2] ([A1] considers diffusion models for optical flow and monocular depth estimation [related tasks], and [A2] specifically looks at incorporating related depth estimation signals). Additional discussion with such methods (beyond the brief note in L545-547 in supplement section C) would be helpful to better contextualize the broader potential impact of the work beyond this specific domain.\n\nReferenced above:\n\n[A1] The Surprising Effectiveness of Diffusion Models for Optical Flow and Monocular Depth Estimation, NeurIPS 2023. ([40] in paper, referenced in supplement section C.)\n\n[A2] Control-A-Video: Controllable Text-to-Video Generation with Diffusion Models, 2023.\n\nQUESTIONS:\nOverall, the preliminary rating of the work leans borderline+; the work offers an exploration of a modern set of diffusion tools and related techniques in a relatively underexplored area (which can have some useful applications downstream), but there remain some questions regarding the analysis and broader novelty. If the authors could address the questions and clarification areas listed in the weaknesses section above for the rebuttal, it would be helpful to inform the discussion phase and final rating.\n\nLIMITATIONS:\nThe authors provide a discussion of limitations, future work, and impacts in the paper and supplement.\n\n---\n\n**Post-rebuttal update:** The rebuttal discussion and additional results help to strengthen the initial impression of the work. Given the reviewer consensus, I am maintaining my rating leaning towards acceptance. (And given that I also believe that the reviewers have adequately addressed concerns of a reviewer who has not updated their review, I am upgrading my rating a bit further since I believe the rating for this work falls between 5-6).", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-12"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper introduces a novel framework that leverages the high temporal resolution of event-based sensors to predict future motion trajectories with unprecedented detail and precision. The authors propose an integration of video diffusion models with event camera data, resulting in an Event-Sequence Diffusion Network. This network is designed to capture the nuances of dynamic scenes and generate video sequences that are both rich in detail and grounded in realistic motion dynamics.\n\nSTRENGTHS:\nIntegration of Event Sequences with Video Diffusion Models: The paper presents the first attempt to combine event sequences with a video diffusion model, creating an event-sequence diffusion model capable of estimating future object motion.\n\nReinforcement Learning for Motion Alignment: The authors propose a method to align the pre-trained event-sequence diffusion model with real-world motion using reinforcement learning techniques, enhancing the fidelity and coherence of the generated motion sequences.\n\nTest-Time Prompt Augmentation: A method is introduced to augment the test-time prompt with high temporal resolution event sequences, which improves the generation performance of the diffusion model.\n\nExtensive Testing and Validation: The authors demonstrate the effectiveness of their approach across various complex scenarios, showcasing its potential for applications in autonomous vehicle guidance, robotic navigation, and interactive media.\n\nPromising Direction for Future Research: The findings suggest a new direction for enhancing the interpretative power and predictive accuracy of computer vision systems, particularly in the context of motion flow prediction.\n\nThe paper's contributions are significant as they push the boundaries of motion estimation in computer vision by harnessing the unique capabilities of event-based sensors and integrating them with advanced diffusion models. The proposed framework opens up new possibilities for accurate prediction of dynamic environments, which is crucial for various real-world applications.\n\nWEAKNESSES:\nFollowing is my concerns:\nWhile the paper demonstrates strong results in controlled scenarios, it may lack evidence of how well the model generalizes to a broader range of real-world conditions, such as various weather effects or low-light environments.\nThe paper could benefit from a more detailed discussion on the computational efficiency of the proposed model. Including runtime analysis and resource requirements would provide a clearer picture of the model's practicality for real-time applications.\nAlthough the paper acknowledges the high temporal resolution of event data, it could delve deeper into the limitations of such data, such as the lack of texture information, and how this might impact the model's performance in complex visual scenes.\nWhile the paper includes some ablation studies, there could be a more thorough investigation into the contribution of each component of the model. This would provide clearer insights into which aspects are most critical to the model's performance.\nThe paper could address how the model performs under noisy conditions or when outliers are present in the event data. This is particularly important given the sensitivity of diffusion models to input quality.\nThere is an opportunity to discuss the explainability and interpretability of the model's predictions. Understanding the factors that contribute to the model's decisions could be valuable for applications in autonomous systems.\nAlthough the paper touches on potential societal impacts, a more detailed discussion on ethical considerations, such as privacy concerns or the potential for misuse, could be beneficial.\nWhile the paper mentions the availability of source code, providing more detailed instructions on how to reproduce the experiments, including the exact versions of software and hardware used, would enhance the reproducibility of the study.\nThe paper focuses on short-term motion estimation. It could discuss the model's capability or limitations in predicting motion over longer time horizons, which is crucial for some applications.\n\nQUESTIONS:\nHas the paper addressed how the model performs under various conditions such as different lighting, weather, or in the presence of occlusions?\nDoes the paper provide evidence of the model's ability to generalize beyond the datasets it was trained on? Are there any specific domains or scenarios where the model might underperform?\nAre there discussions on the computational resources required to run the model, and is it scalable for use in resource-constrained environments?\nDoes the paper discuss any potential misuse of the technology, such as in surveillance or other applications that might infringe on individual rights?\nAre there discussions on how the technology could affect different demographic groups differently, potentially exacerbating existing biases?\nAre there any regulatory or compliance issues related to the deployment of such technology, especially in sectors like automotive or robotics?\n\nLIMITATIONS:\nSee above", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-14"}