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REVIEW 3 major objections 5 minor 44 references

Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation

T0 review · 3 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Event motion cues beat frame-only baselines at x24 video interpolation.

desk verdict A well-ablated adapter recipe for event-guided DiT interpolation, but the 'consistently outperforms' claim is too strong and the Pexels test set may overlap the EvPexels training data. read the letter →

arxiv 2608.10479 v2 pith:7HH6JBXS submitted 2026-08-11 cs.CV

Guixu Lin , Yuyang Yu , Xiang Ji , Linyao Chen , Zhengwei Yin , Mengshun Hu , Mingdeng Cao , Shengfeng He
show 1 more author
Yinqiang Zheng
This is my paper · ORCID
classification cs.CV
keywords eventcamerasvideoframeinterpolationdiffusiontransformerimage-to-videoopticalflowimagewarpedeventscontrastmaximizationadapterfine-tuning
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that sparse, asynchronous event-camera data—converted into image warped events (IWEs) and bidirectional sparse optical flow via contrast maximization—can be injected into a pre-trained DiT-based image-to-video diffusion model through two lightweight adapters, improving video frame interpolation across large temporal gaps. The proposal matters because event cameras capture high-temporal-resolution motion that frame-only methods miss, and the adapter approach avoids training an event-assisted generative model from scratch. On the real BS-ERGB benchmark the method sets state-of-the-art perceptual metrics (LPIPS, FID, FVD), and on the synthetic DAVIS and Pexels benchmarks it leads on all five reported metrics at x24 interpolation. The paper also contributes EvPexels, a synthetic event-video dataset of about 390,000 frames, to support training and benchmarking.

What carries the argument

The engine of the method is the pairing of two event-derived signals—Image Warped Events and bidirectional sparse optical flow—both computed from the raw event stream via contrast maximization between consecutive latent frames. The IWE encoder is a compact 3D-convolution network whose features are added element-wise to the input latents, supplying edge-like structural cues. The flow-based alignment-and-fusion adapter warps DiT features from the previous and next latent frames toward the current frame using the estimated flows, aggregates them with the current frame's features, and adds the fused result as a residual correction before selected DiT blocks. This explicit motion-guided feature warping is the mechanism that enforces temporal coherence, supported by LoRA fine-tuning of the frozen backbone.

What would settle it

Measure the estimated bidirectional flows against ground-truth optical flow on a synthetic scene with known dense motion; if the event-only flows are systematically wrong in occluded or textureless regions and those errors propagate to the interpolated frames (visible as warping artifacts or drops in FVD/LPIPS), the core assumption fails.

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Extended reading notes

Core claim

The central claim is that event streams, when expressed as IWEs (edge-aligned images obtained by warping events along flow) and bidirectional optical flow, provide spatially and temporally aligned guidance that a frozen DiT-based image-to-video model can consume with minimal architectural change. The paper shows that adding an IWE encoder that injects edge features into the latent input, plus flow-based alignment-and-fusion adapters that warp DiT features from neighboring latent frames toward the current frame before selected blocks, reduces motion blur, structural distortion, and temporal inconsistency relative to frame-only interpolation. The authors demonstrate this on real and synthetic benchmarks, reporting that the method outperforms existing state-of-the-art approaches: state-of-the-art perceptual quality on BS-ERGB and best all-metric results on DAVIS and Pexels at x24 interpolation. They further argue that this proves event-derived flow and IWE can serve as drop-in guidance for large generative interpolators, circumventing the need to adapt sparse event streams directly into dense grid-based representations.

Load-bearing premise

The load-bearing premise is that the event-only optical flow estimates, produced by contrast maximization in temporal segments between latent frames, stay accurate enough to warp DiT features across the 4x temporal compression, even in regions with occlusions, low event density, or large appearance changes.

Editorial extensions

If this is right

  • Event cameras become a practical conditioning signal for large generative video models, enabling high-quality interpolation at x24 temporal gaps with only adapter-level fine-tuning.
  • The same IWE-plus-flow adapter recipe could be transplanted to other DiT-based image-to-video backbones, since it leaves the base denoiser frozen.
  • The released synthetic event-video dataset EvPexels allows other researchers to train and evaluate event-guided interpolation without collecting real event data.
  • The ablation results imply that explicit motion-guided feature warping is more effective than simply concatenating event features into the latent input, guiding future designs for temporal conditioning.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Because the flow-warping adapter is agnostic to how the flow was produced, the same architecture could in principle be fed optical flow estimated from RGB frames or from a hybrid event-frame estimator, making the approach portable to frame-only interpolation settings.
  • The accuracy of the event-only flow is the ceiling on the gains: in occluded or low-event-density regions, noisy warps could actively corrupt features, so a learned flow-refinement step or confidence-weighted fusion would be a natural, testable extension.
  • The benchmark pattern—perceptual metrics winning while PSNR trails traditional methods on BS-ERGB—suggests the model trades pixel-exactness for perceived realism; a user study or task-based evaluation would quantify whether that trade-off is preferred.
  • The ablation showing injection into the first DiT blocks favors PSNR/SSIM while last blocks favor LPIPS/FID/FVD hints at a complementary schedule: injecting at both early and late blocks might combine reconstruction and perceptual strengths.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper proposes an adapter-based framework for event-guided video frame interpolation built on a frozen DiT-based image-to-video diffusion model (Wan2.1 FLF2V). Event streams are converted into Image Warped Events (IWEs) and bidirectional sparse optical flow by contrast maximization; an IWE encoder injects edge-aware structural features into the latent input, and a flow-based alignment-and-fusion adapter warps DiT features from neighboring latent frames before selected DiT blocks. The model is fine-tuned with LoRA on a newly introduced synthetic dataset, EvPexels, plus the BS-ERGB training split. Experiments at x24 interpolation on BS-ERGB, DAVIS, and Pexels report state-of-the-art perceptual metrics on BS-ERGB and best all-metric results on DAVIS and Pexels.

Significance. If the empirical claims hold, the paper offers a practically valuable recipe for incorporating event data into large pre-trained I2V diffusion models without retraining from scratch, and the EvPexels dataset is a potentially useful community resource. The design is clean: event cues are converted into modalities compatible with standard diffusion control, and the ablations in Tables 3-5 support the contribution of each component. However, the headline claim of consistent state-of-the-art performance currently rests on results that are not fully verified due to a possible train/test overlap on the Pexels benchmark, as well as an abstract that overstates the BS-ERGB numbers.

major comments (3)
  1. [4.1.1, 4.1.2, Table 2] The Pexels test set is drawn from the same platform used to construct the EvPexels training set, but the paper reports no source-video ID check, frame-level deduplication, or temporal-segment overlap analysis between the 30 Pexels test clips and the 1,100 EvPexels training sequences. If any of the test clips overlap with training content, the Pexels columns in Table 2 are inflated and the headline claim of consistent state-of-the-art performance is unsupported on one of the three benchmarks. Please report the exact source video IDs/URLs for both sets, verify disjointness, and if any overlap exists recompute the Pexels metrics on a strictly disjoint subset.
  2. [Abstract, 4.2.1, Table 1] The abstract's statement that the method 'consistently outperforms existing state-of-the-art approaches' is not supported by Table 1: on BS-ERGB, CBMNet-Large achieves higher PSNR (25.306 vs. 23.261) and SSIM (0.7120 vs. 0.704), and TimeLens achieves higher PSNR (24.704 vs. 23.261). The body text at the end of Section 4.2.1 correctly qualifies the result as state-of-the-art on perceptual metrics, so the abstract and conclusion should be revised to match, e.g., 'state-of-the-art perceptual quality on BS-ERGB and best all-metric results on DAVIS and Pexels.'
  3. [3.2.1, Eq. (6)-(8), 4.4] The whole pipeline depends on event-only sparse bidirectional optical flow to warp DiT features across the temporally compressed latent space, but the paper provides no quantitative accuracy check of these flow estimates against ground truth; Section 4.4 offers only visual inspection. If the flows are noisy or incomplete in occluded, low-event-density, or large-appearance-change regions, the warping in Eq. (7) could corrupt features rather than align them. Please add a quantitative flow-error evaluation, e.g., endpoint error on a synthetic benchmark with ground-truth flow, and discuss failure modes for the event-only flow estimates.
minor comments (5)
  1. [4.3] The heading 'Ablation Studys' contains a typo and should read 'Ablation Study.'
  2. [Table 2] The header 'DA VIS' has an erroneous space and should read 'DAVIS.'
  3. [References] Reference [12] contains the typo 'W ACV' in the venue name; it should be 'WACV.'
  4. [Figure 2] The notation in Figure 2, especially the arrow labels such as 'w' and the relationship between IWEs, flows, and the alignment adapters, is dense and not fully explained in the caption; a more detailed caption or a legend would aid reproducibility.
  5. [4.1.3, 4.3.1] The main fine-tuning uses 4,000 steps on 8 NVIDIA A800 GPUs, while the ablations are trained for 5,400 steps on a single GPU; this difference in training budget should be stated clearly so that the ablation numbers in Table 3 are not directly compared to Table 1 without this caveat.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the event-cue injection is an empirically trained adapter; the only circularity-adjacent observation is a minor, non-load-bearing self-citation.

full rationale

The paper's central claim is that event-derived IWEs and bidirectional optical flow, injected via adapters into a frozen Wan2.1 DiT, improve video frame interpolation. This claim is tested empirically on BS-ERGB, DAVIS, and Pexels against multiple baselines. No equation in the derivation reduces to the method's own output: the event representations are computed by an off-the-shelf contrast maximization method [24,26], the adapters are trained on separate datasets, and the evaluation uses held-out test frames. There is no fitted parameter that is then renamed as a prediction, and no uniqueness theorem or ansatz is imported from the authors' own prior work to force the design. The only self-citation is reference [17] in the related-work survey of event-based VFI methods; it is not load-bearing for any architectural choice or result. The data-leakage concern about EvPexels training videos and Pexels test clips (Sec. 4.1.1 vs. Sec. 4.1.2) is a legitimate empirical-validity issue, but it is not circular reasoning: the Pexels test numbers are measurements, not consequences of the training-set definition. Thus the paper is self-contained against external benchmarks, and the circularity score is low, reflecting only the minor non-load-bearing self-citation.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The paper introduces no new physical entities. It relies on domain assumptions about event-to-flow conversion, the adaptability of a frozen DiT, and the representativeness of synthetic events. The free parameters are standard hyperparameters and a design choice about injection position, not deep scientific constants.

free parameters (3)
  • Injection block position = first two DiT blocks
    Chosen by ablation (Tab. 4) to prioritize PSNR/SSIM; the central method depends on this choice.
  • Number of alignment adapters = 2
    Set by hand for efficiency, not swept; the paper does not investigate other counts.
  • Learning rate = 1e-4
    Standard choice, not justified; affects training but not a novel contribution.
assumptions (3)
  • domain assumption Event streams can be converted into IWEs and sparse bidirectional optical flow that provide edge and motion cues compatible with DiT latent features.
    Stated in Sec. 3.2.1; if false, the entire adapter design fails.
  • domain assumption The pre-trained Wan2.1 FLF2V model can be adapted to event conditioning with only LoRA fine-tuning and two alignment adapters, without architectural changes.
    Assumed in Sec. 3.2.2 and 3.2.3; the experimental gains depend on this compatibility.
  • domain assumption Vid2e-simulated events from RGB videos (EvPexels) are representative of real event data for training.
    Sec. 4.1.1; the model is trained on synthetic events and evaluated on real BS-ERGB, so transferability is assumed.

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Cite this review

Pith. "Pith review of Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation." pith.science (2026). https://pith.science/paper/7HH6JBXS

@misc{pith2026260810479,
  author       = {Pith},
  title        = {Pith review of: Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7HH6JBXS}},
  note         = {Machine review of arXiv:2608.10479}
}
read the original abstract

Latent diffusion models have recently advanced video frame interpolation by synthesizing intermediate frames between input images. However, handling large temporal gaps and complex motion remains challenging, often resulting in motion blur, structural distortions, and temporal inconsistencies. Event cameras provide high-temporal-resolution motion cues that are well suited for bridging these gaps and improving interpolation quality. To exploit this advantage without training an event-assisted model from scratch, we propose an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model with minimal architectural changes. Specifically, our method leverages Image Warped Events (IWEs) and bidirectional sparse optical flow to provide spatially and temporally aligned guidance during generation. By injecting these event-guided structural and motion cues into the diffusion process, our approach reduces interpolation artifacts and improves both reconstruction fidelity and temporal coherence. Experimental results on real and synthetic benchmarks show that our method consistently outperforms existing state-of-the-art approaches. The project page is at https://joseph-lin-tech.github.io/BridgeEventDiT-VFI/.

Figures

Figures reproduced from arXiv: 2608.10479 by the authors.

Figure 1
Figure 1. We propose an event-augmented approach for Diffusion Transformer (DiT)-based video frame interpolation, inte [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Illustration of Our Framework. (a) We extract bidirectional sparse optical flow and IWEs from the input event stream [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Visual comparison of VFI methods on the BS-ERGB test dataset (time [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: Visual comparison between VDM-EVFI-Wan2.1 and our method on the BS-ERGB dataset.(time [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Visual comparison between VDM-EVFI-Wan2.1 and Ours method on the Davis dataset (time [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: Illustration of the optical flow & IWE. both IWE and optical flow features play essential roles in enhancing interpolation quality. Directly injecting flow information as input (without warping) yields inferior performance compared to the warping-based approach, highli…

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Works this paper leans on

44 extracted references · 30 canonical work pages

  1. [1]

    Wenbo Bao, Wei-Sheng Lai, Chao Ma, Xiaoyun Zhang, Zhiyong Gao, and Ming- Hsuan Yang. 2019. Depth-aware video frame interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    work page 2019

  2. [2]

    Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, et al. 2023. Stable video diffusion: Scaling latent video diffusion models to large datasets.arXiv preprint arXiv:2311.15127(2023)

    arXiv 2023

  3. [3]

    Ryan Burgert, Yuancheng Xu, Wenqi Xian, Oliver Pilarski, Pascal Clausen, Ming- ming He, Li Ma, Yitong Deng, Lingxiao Li, Mohsen Mousavi, et al. 2025. Go-with- the-flow: Motion-controllable video diffusion models using real-time warped noise. InProceedings of the Computer Vision and Pattern Recognition Conference. 13–23

    2025

  4. [4]

    Jingxi Chen, Brandon Y Feng, Haoming Cai, Tianfu Wang, Levi Burner, De- hao Yuan, Cornelia Fermuller, Christopher A Metzler, and Yiannis Aloimonos

    work page

  5. [5]

    Duolikun Danier, Fan Zhang, and David Bull. 2024. Ldmvfi: Video frame inter- polation with latent diffusion models. InAAAI

    work page 2024

  6. [6]

    Haiwen Feng, Zheng Ding, Zhihao Xia, Simon Niklaus, Victoria Abrevaya, Michael J Black, and Xuaner Zhang. 2024. Explorative Inbetweening of Time and Space.arXiv preprint arXiv:2403.14611(2024)

    arXiv 2024

  7. [7]

    Daniel Gehrig, Mathias Gehrig, Javier Hidalgo-Carrió, and Davide Scaramuzza

    work page

  8. [8]

    Weihua He, Kaichao You, Zhendong Qiao, Xu Jia, Ziyang Zhang, Wenhui Wang, Huchuan Lu, Yaoyuan Wang, and Jianxing Liao. 2022. TimeReplayer: Unlocking the potential of event cameras for video interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    work page 2022

Show all 44 references
  1. [9]

    Zhewei Huang, Tianyuan Zhang, Wen Heng, Boxin Shi, and Shuchang Zhou

  2. [10]

    Siddhant Jain, Daniel Watson, Eric Tabellion, Ben Poole, Janne Kontkanen, et al

  3. [11]

    Zeyinzi Jiang, Zhen Han, Chaojie Mao, Jingfeng Zhang, Yulin Pan, and Yu Liu

  4. [12]

    Pritam P Karmokar, Quan H Nguyen, and William J Beksi. 2025. Secrets of Edge-Informed Contrast Maximization for Event-Based Vision. In2025 IEEE/CVF Winter Conference on Applications of Computer Vision (W ACV). IEEE, 630–639

    2025

  5. [13]

    Taewoo Kim, Yujeong Chae, Hyun-Kurl Jang, and Kuk-Jin Yoon. 2023. Event- Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields. InIEEE Conf. Comput. Vis. Pattern Recog

    2023

  6. [14]

    InIEEE Conf

    Video interpolation with diffusion models. InIEEE Conf. Comput. Vis. Pattern Recog

  7. [15]

    Bo Li, Kaitao Xue, Bin Liu, and Yu-Kun Lai. 2023. Bbdm: Image-to-image transla- tion with brownian bridge diffusion models. InIEEE Conf. Comput. Vis. Pattern Recog

    2023

  8. [16]

    Zhen Li, Zuo-Liang Zhu, Ling-Hao Han, Qibin Hou, Chun-Le Guo, and Ming- Ming Cheng. 2023. AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    2023

  9. [17]

    Guixu Lin, Jin Han, Mingdeng Cao, Zhihang Zhong, and Yinqiang Zheng. 2023. Event-guided Frame Interpolation and Dynamic Range Expansion of Single Rolling Shutter Image. InProceedings of the 31st ACM International Conference on Multimedia

    2023

  10. [18]

    Yuhan Liu, Yongjian Deng, Hao Chen, and Zhen Yang. 2024. Video Frame Interpolation via Direct Synthesis with the Event-based Reference. InIEEE Conf. Comput. Vis. Pattern Recog

    2024

  11. [19]

    Lingtong Kong, Boyuan Jiang, Donghao Luo, Wenqing Chu, Xiaoming Huang, Ying Tai, Chengjie Wang, and Jie Yang. 2022. Ifrnet: Intermediate feature refine network for efficient frame interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    2022

  12. [20]

    Yongrui Ma, Shi Guo, Yutian Chen, Tianfan Xue, and Jinwei Gu. 2024. TimeLens- XL: Real-Time Event-Based Video Frame Interpolation with Large Motion. In Eur. Conf. Comput. Vis

    2024

  13. [21]

    Simon Niklaus, Long Mai, and Feng Liu. 2017. Video frame interpolation via adaptive separable convolution. InInt. Conf. Comput. Vis

    2017

  14. [22]

    Jordi Pont-Tuset, Federico Perazzi, Sergi Caelles, Pablo Arbeláez, Alex Sorkine- Hornung, and Luc Van Gool. 2017. The 2017 davis challenge on video object segmentation.arXiv preprint arXiv:1704.00675(2017)

    2017 arXiv

  15. [23]

    Liao Shen, Tianqi Liu, Huiqiang Sun, Xinyi Ye, Baopu Li, Jianming Zhang, and Zhiguo Cao. 2024. Dreammover: Leveraging the prior of diffusion models for image interpolation with large motion. InEur. Conf. Comput. Vis

    2024

  16. [24]

    Zonglin Lyu, Ming Li, Jianbo Jiao, and Chen Chen. 2024. Frame Interpolation with Consecutive Brownian Bridge Diffusion.arXiv preprint arXiv:2405.05953 (2024)

    2024 arXiv

  17. [25]

    Tomás Soucek and Jakub Lokoc. 2024. Transnet v2: An effective deep network architecture for fast shot transition detection. InProceedings of the 32nd ACM International Conference on Multimedia. 11218–11221

    2024

  18. [26]

    Timo Stoffregen and Lindsay Kleeman. 2019. Event Cameras, Contrast Maxi- mization and Reward Functions: An Analysis. InIEEE Conf. Comput. Vis. Pattern Recog

    2019

  19. [27]

    Lei Sun, Christos Sakaridis, Jingyun Liang, Peng Sun, Jiezhang Cao, Kai Zhang, Qi Jiang, Kaiwei Wang, and Luc Van Gool. 2023. Event-based frame interpolation with ad-hoc deblurring. InIEEE Conf. Comput. Vis. Pattern Recog

    2023

  20. [28]

    Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig, Stamatios Georgoulis, Yuanyou Li, and Davide Scaramuzza. 2022. Time lens++: Event-based frame interpolation with parametric non-linear flow and multi-scale fusion. InIEEE Conf. Comput. Vis. Pattern Recog

    2022

  21. [29]

    Shintaro Shiba, Yannick Klose, Yoshimitsu Aoki, and Guillermo Gallego. 2024. Secrets of event-based optical flow, depth and ego-motion estimation by contrast maximization.IEEE Transactions on Pattern Analysis and Machine Intelligence (2024), 1–18

    2024

  22. [30]

    Vikram Voleti, Alexia Jolicoeur-Martineau, and Chris Pal. 2022. Mcvd-masked conditional video diffusion for prediction, generation, and interpolation.Adv. Neural Inform. Process. Syst.(2022)

    2022

  23. [31]

    Team Wan, Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, et al. 2025. Wan: Open and advanced large-scale video generative models.arXiv preprint arXiv:2503.20314(2025)

    2025 arXiv

  24. [32]

    Xiaojuan Wang, Boyang Zhou, Brian Curless, Ira Kemelmacher-Shlizerman, Aleksander Holynski, and Steven M Seitz. 2024. Generative Inbetweening: Adapting Image-to-Video Models for Keyframe Interpolation.arXiv preprint arXiv:2408.15239(2024)

    2024 arXiv

  25. [33]

    Serin Yang, Taesung Kwon, and Jong Chul Ye. 2024. ViBiDSampler: Enhanc- ing Video Interpolation Using Bidirectional Diffusion Sampler.arXiv preprint arXiv:2410.05651(2024)

    2024 arXiv

  26. [34]

    Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, and Davide Scaramuzza. 2021. Time lens: Event-based video frame interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    2021

  27. [35]

    Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, and Limin Wang. 2023. Extracting motion and appearance via inter-frame attention for efficient video frame interpolation. InIEEE Conf. Comput. Vis. Pattern Recog

    2023

  28. [36]

    Xiang Zhang and Lei Yu. 2022. Unifying motion deblurring and frame interpola- tion with events. InIEEE Conf. Comput. Vis. Pattern Recog

    2022

  29. [37]

    Yaping Zhao, Pei Zhang, Chutian Wang, and Edmund Y Lam. 2024. Controllable unsupervised event-based video generation. In2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2278–2284

    2024

  30. [38]

    Tianyi Zhu, Dongwei Ren, Qilong Wang, Xiaohe Wu, and Wangmeng Zuo

  31. [39]

    Zhiyang Yu, Yu Zhang, Deyuan Liu, Dongqing Zou, Xijun Chen, Yebin Liu, and Jimmy S Ren. 2021. Training weakly supervised video frame interpolation with events. InInt. Conf. Comput. Vis

    2021

  32. [44]

    Generative Inbetweening through Frame-wise Conditions-Driven Video Generation.arXiv preprint arXiv:2412.11755(2024)

    2024 arXiv

  33. [2020]

    InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Video to events: Recycling video datasets for event cameras. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 3586– 3595

  34. [2022]

    In European Conference on Computer Vision

    Real-time intermediate flow estimation for video frame interpolation. In European Conference on Computer Vision. Springer, 624–642

  35. [2024]

    Repurposing Pre-trained Video Diffusion Models for Event-based Video Interpolation.arXiv preprint arXiv:2412.07761(2024)

    2024 arXiv

  36. [2025]

    Vace: All-in-one video creation and editing.arXiv preprint arXiv:2503.07598 (2025)

    2025 arXiv

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