TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion

• URL: http://arxiv.org/abs/2505.03116v1
• Date: Tue, 06 May 2025 02:12:19 GMT
• Title: TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion
• Authors: Haoyue Liu, Jinghan Xu, Yi Chang, Hanyu Zhou, Haozhi Zhao, Lin Wang, Luxin Yan,
• Abstract summary: A hurdle for event-based video frame (VFI) is how to deal with non-linear motion.<n>We propose a novel continuous point tracking-based VFI framework, named TimeTracker.<n>Our method outperforms prior arts in both motion estimation and frame quality.
• Score: 18.191333256398845
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods, thanks to the event cameras' advantages, such as high temporal resolution. A hurdle for event-based VFI is how to effectively deal with non-linear motion, caused by the dynamic changes in motion direction and speed within the scene. Existing methods either use events to estimate sparse optical flow or fuse events with image features to estimate dense optical flow. Unfortunately, motion errors often degrade the VFI quality as the continuous motion cues from events do not align with the dense spatial information of images in the temporal dimension. In this paper, we find that object motion is continuous in space, tracking local regions over continuous time enables more accurate identification of spatiotemporal feature correlations. In light of this, we propose a novel continuous point tracking-based VFI framework, named TimeTracker. Specifically, we first design a Scene-Aware Region Segmentation (SARS) module to divide the scene into similar patches. Then, a Continuous Trajectory guided Motion Estimation (CTME) module is proposed to track the continuous motion trajectory of each patch through events. Finally, intermediate frames at any given time are generated through global motion optimization and frame refinement. Moreover, we collect a real-world dataset that features fast non-linear motion. Extensive experiments show that our method outperforms prior arts in both motion estimation and frame interpolation quality.

Related papers

• Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
  Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts.<n>Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion.<n>We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
  arXiv  Detail & Related papers  (2025-03-25T17:58:48Z)
• EMoTive: Event-guided Trajectory Modeling for 3D Motion Estimation [59.33052312107478]
  Event cameras offer possibilities for 3D motion estimation through continuous adaptive pixel-level responses to scene changes.<n>This paper presents EMove, a novel event-based framework that models-uniform trajectories via event-guided parametric curves.<n>For motion representation, we introduce a density-aware adaptation mechanism to fuse spatial and temporal features under event guidance.<n>The final 3D motion estimation is achieved through multi-temporal sampling of parametric trajectories, flows and depth motion fields.
  arXiv  Detail & Related papers  (2025-03-14T13:15:54Z)
• Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields [39.214857326425204]
  Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames.<n>We propose a novel event-based VFI framework with cross-modal asymmetric bidirectional motion field estimation.<n>Our method shows significant performance improvement over the state-of-the-art VFI methods on various datasets.
  arXiv  Detail & Related papers  (2025-02-19T13:40:43Z)
• Event-Based Tracking Any Point with Motion-Augmented Temporal Consistency [58.719310295870024]
  This paper presents an event-based framework for tracking any point.<n>It tackles the challenges posed by spatial sparsity and motion sensitivity in events.<n>It achieves 150% faster processing with competitive model parameters.
  arXiv  Detail & Related papers  (2024-12-02T09:13:29Z)
• DATAP-SfM: Dynamic-Aware Tracking Any Point for Robust Structure from Motion in the Wild [85.03973683867797]
  This paper proposes a concise, elegant, and robust pipeline to estimate smooth camera trajectories and obtain dense point clouds for casual videos in the wild. We show that the proposed method achieves state-of-the-art performance in terms of camera pose estimation even in complex dynamic challenge scenes.
  arXiv  Detail & Related papers  (2024-11-20T13:01:16Z)
• Motion Segmentation for Neuromorphic Aerial Surveillance [42.04157319642197]
  Event cameras offer superior temporal resolution, superior dynamic range, and minimal power requirements. Unlike traditional frame-based sensors that capture redundant information at fixed intervals, event cameras asynchronously record pixel-level brightness changes. We introduce a novel motion segmentation method that leverages self-supervised vision transformers on both event data and optical flow information.
  arXiv  Detail & Related papers  (2024-05-24T04:36:13Z)
• Event-based Video Frame Interpolation with Edge Guided Motion Refinement [28.331148083668857]
  We introduce an end-to-end E-VFI learning method to efficiently utilize edge features from event signals for motion flow and warping enhancement. Our method incorporates an Edge Guided Attentive (EGA) module, which rectifies estimated video motion through attentive aggregation. Experiments on both synthetic and real datasets show the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2024-04-28T12:13:34Z)
• Motion-Aware Video Frame Interpolation [49.49668436390514]
  We introduce a Motion-Aware Video Frame Interpolation (MA-VFI) network, which directly estimates intermediate optical flow from consecutive frames. It not only extracts global semantic relationships and spatial details from input frames with different receptive fields, but also effectively reduces the required computational cost and complexity.
  arXiv  Detail & Related papers  (2024-02-05T11:00:14Z)
• Video frame interpolation for high dynamic range sequences captured with dual-exposure sensors [24.086089662881044]
  Video frame (VFI) enables many important applications that might involve the temporal domain. One of the key challenges is handling high dynamic range scenes in the presence of complex motion.
  arXiv  Detail & Related papers  (2022-06-19T20:29:34Z)
• Implicit Motion Handling for Video Camouflaged Object Detection [60.98467179649398]
  We propose a new video camouflaged object detection (VCOD) framework. It can exploit both short-term and long-term temporal consistency to detect camouflaged objects from video frames.
  arXiv  Detail & Related papers  (2022-03-14T17:55:41Z)

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.