EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring

• URL: http://arxiv.org/abs/2608.08066v1
• Date: Sat, 08 Aug 2026 11:10:26 GMT
• Title: EvBS: Event-guided Blur Synthesis for Domain-adaptive Motion Deblurring
• Abstract summary: EvBS is an event-guided blur synthesis framework.<n>It generates diverse training pairs for calibrating pre-trained models to the target domain.<n>EvBS effectively enhances the robustness of existing deblurring models on unseen testing datasets.
• Score: 22.317727241744702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motion deblurring has achieved remarkable progress with deep learning, yet pre-trained deblurring models often suffer from performance degradation in real-world scenarios due to the domain shift between training and testing distributions. To remedy this, we propose EvBS, an event-guided blur synthesis framework that generates diverse training pairs for calibrating pre-trained models to the target domain. While existing methods are constrained by the inherent entanglement between motion and visual content, our method leverages the high temporal resolution of event cameras to effectively decouple them. This enables us to utilize not only the intrinsic motion that is inherent to the given content but also extrinsic motion transferred from different sources within the target domain, thereby facilitating effective adaptation via fine-tuning. Specifically, EvBS comprises two complementary strategies: Intrinsic-Blur Synthesis, which blurs sharp contents with their own motion patterns, and Extrinsic-Blur Synthesis, which transfers motion from blurry patches to distinct sharp content. This approach generates a diverse set of training pairs that break the inherent constraints of naturally coupled motion and content, resulting in enhanced domain-adaptive deblurring performance. Extensive experiments on multiple benchmarks demonstrate that EvBS effectively enhances the robustness of existing deblurring models on unseen testing datasets.

Related papers

• Bridging Event Streams and DiT: Event-Guided Video Frame Interpolation [75.95737622994548]
  Event cameras provide high-resolution motion cues that are well suited for bridging temporal gaps.<n>We propose an adapter-based framework that incorporates event-derived cues into a pre-trained image-to-video diffusion model.
  arXiv  Detail & Related papers  (2026-08-11T04:47:44Z)
• From Diffusion To Flow: Efficient Motion Generation In MotionGPT3 [0.0]
  MotionGPT3 combines a learned continuous motion latent space with a diffusion-based prior for text-conditioned synthesis.<n>We compare diffusion and rectified flow objectives within the MotionGPT3 framework.<n>Experiments show that rectified flow converges in fewer training epochs, reaches strong test performance earlier, and matches or exceeds diffusion-based motion quality.
  arXiv  Detail & Related papers  (2026-03-23T09:44:07Z)
• Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations [8.133207162076877]
  We propose a framework for grasp pose generation and object trajectory imitation under the video-based Learning-from-Demonstration (LfD) paradigm.<n>Rather than directly imitating human hand motions, our method treats demonstrations as object-centric guides.<n>We validate our approach in both simulation and real-world experiments across diverse real-world manipulation tasks.
  arXiv  Detail & Related papers  (2025-09-25T03:11:07Z)
• Unified modality separation: A vision-language framework for unsupervised domain adaptation [60.8391821117794]
  Unsupervised domain adaptation (UDA) enables models trained on a labeled source domain to handle new unlabeled domains.<n>We propose a unified modality separation framework that accommodates both modality-specific and modality-invariant components.<n>Our methods achieve up to 9% performance gain with 9 times of computational efficiencies.
  arXiv  Detail & Related papers  (2025-08-07T02:51:10Z)
• Motion-Aware Generative Frame Interpolation [23.380470636851022]
  Flow-based frame methods ensure motion stability through estimated intermediate flow but often introduce severe artifacts in complex motion regions.<n>Recent generative approaches, boosted by large-scale pre-trained video generation models, show promise in handling intricate scenes.<n>We propose Motion-aware Generative frame (MoG) that synergizes intermediate flow guidance with generative capacities to enhance fidelity.
  arXiv  Detail & Related papers  (2025-01-07T11:03:43Z)
• MATE: Motion-Augmented Temporal Consistency for Event-based Point Tracking [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>To resolve ambiguities caused by event sparsity, a motion-guidance module incorporates kinematic vectors into the local matching process.<n>The method improves the $Survival_50$ metric by 17.9% over event-only tracking of any point baseline.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• Training-Free Semantic Video Composition via Pre-trained Diffusion Model [96.0168609879295]
  Current approaches, predominantly trained on videos with adjusted foreground color and lighting, struggle to address deep semantic disparities beyond superficial adjustments. We propose a training-free pipeline employing a pre-trained diffusion model imbued with semantic prior knowledge. Experimental results reveal that our pipeline successfully ensures the visual harmony and inter-frame coherence of the outputs.
  arXiv  Detail & Related papers  (2024-01-17T13:07:22Z)
• Scalable Motion Style Transfer with Constrained Diffusion Generation [27.330473515686187]
  Current training of style transfer systems relies on consistency losses across style domains to preserve contents. Recent image transfer works show the potential of independent training on each domain by leveraging implicit bridging between diffusion models. We address this by imposing biased sampling in backward diffusion while maintaining the domain independence in the training stage.
  arXiv  Detail & Related papers  (2023-12-12T14:28:31Z)
• Generalizing Event-Based Motion Deblurring in Real-World Scenarios [62.995994797897424]
  Event-based motion deblurring has shown promising results by exploiting low-latency events. We propose a scale-aware network that allows flexible input spatial scales and enables learning from different temporal scales of motion blur. A two-stage self-supervised learning scheme is then developed to fit real-world data distribution.
  arXiv  Detail & Related papers  (2023-08-11T04:27:29Z)
• Spatiotemporal Multi-scale Bilateral Motion Network for Gait Recognition [3.1240043488226967]
  In this paper, motivated by optical flow, the bilateral motion-oriented features are proposed. We develop a set of multi-scale temporal representations that force the motion context to be richly described at various levels of temporal resolution.
  arXiv  Detail & Related papers  (2022-09-26T01:36:22Z)
• Motion Deblurring with Real Events [50.441934496692376]
  We propose an end-to-end learning framework for event-based motion deblurring in a self-supervised manner. Real-world events are exploited to alleviate the performance degradation caused by data inconsistency.
  arXiv  Detail & Related papers  (2021-09-28T13:11:44Z)
• EAN: Event Adaptive Network for Enhanced Action Recognition [66.81780707955852]
  We propose a unified action recognition framework to investigate the dynamic nature of video content. First, when extracting local cues, we generate the spatial-temporal kernels of dynamic-scale to adaptively fit the diverse events. Second, to accurately aggregate these cues into a global video representation, we propose to mine the interactions only among a few selected foreground objects by a Transformer.
  arXiv  Detail & Related papers  (2021-07-22T15:57:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.