TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video

• URL: http://arxiv.org/abs/2411.18671v2
• Date: Fri, 26 Sep 2025 08:17:26 GMT
• Title: TAPTRv3: Spatial and Temporal Context Foster Robust Tracking of Any Point in Long Video
• Authors: Jinyuan Qu, Hongyang Li, Shilong Liu, Tianhe Ren, Zhaoyang Zeng, Lei Zhang,
• Abstract summary: We present TAPTRv3, a point tracking framework that works fine in regular videos but tends to fail in long videos.<n>We use both spatial and temporal context to bring better feature querying along the spatial and temporal dimensions.<n> TAPTRv3 surpasses TAPTRv2 by a large margin on most of the challenging datasets and obtains state-of-the-art performance.
• Score: 36.27277088961125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, built upon TAPTRv2, we present TAPTRv3. TAPTRv2 is a simple yet effective DETR-like point tracking framework that works fine in regular videos but tends to fail in long videos. TAPTRv3 improves TAPTRv2 by addressing its shortcomings in querying high-quality features from long videos, where the target tracking points normally undergo increasing variation over time. In TAPTRv3, we propose to utilize both spatial and temporal context to bring better feature querying along the spatial and temporal dimensions for more robust tracking in long videos. For better spatial feature querying, we identify that off-the-shelf attention mechanisms struggle with point-level tasks and present Context-aware Cross-Attention (CCA). CCA introduces spatial context into the attention mechanism to enhance the quality of attention scores when querying image features. For better temporal feature querying, we introduce Visibility-aware Long-Temporal Attention (VLTA), which conducts temporal attention over past frames while considering their corresponding visibilities. This effectively addresses the feature drifting problem in TAPTRv2 caused by its RNN-like long-term modeling. TAPTRv3 surpasses TAPTRv2 by a large margin on most of the challenging datasets and obtains state-of-the-art performance. Even when compared with methods trained on large-scale extra internal data, TAPTRv3 still demonstrates superiority.

Related papers

• Repurposing Video Diffusion Transformers for Robust Point Tracking [35.486648006768256]
  Existing methods rely on shallow convolutional backbones such as ResNet that process frames independently.<n>We find that video Transformers (DiTs) inherently exhibit strong point tracking capability and robustly handle dynamic motions.<n>Our work validates video DiT features as an effective and efficient foundation for point tracking.
  arXiv  Detail & Related papers  (2025-12-23T18:54:10Z)
• Exploiting Temporal State Space Sharing for Video Semantic Segmentation [53.8810901249897]
  Video semantic segmentation (VSS) plays a vital role in understanding the temporal evolution of scenes. Traditional methods often segment videos frame-by-frame or in a short temporal window, leading to limited temporal context, redundant computations, and heavy memory requirements. We introduce a Temporal Video State Space Sharing architecture to leverage Mamba state space models for temporal feature sharing. Our model features a selective gating mechanism that efficiently propagates relevant information across video frames, eliminating the need for a memory-heavy feature pool.
  arXiv  Detail & Related papers  (2025-03-26T01:47:42Z)
• 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. Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion. 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)
• Online Dense Point Tracking with Streaming Memory [54.22820729477756]
  Dense point tracking is a challenging task requiring the continuous tracking of every point in the initial frame throughout a substantial portion of a video. Recent point tracking algorithms usually depend on sliding windows for indirect information propagation from the first frame to the current one. We present a lightweight and fast model with textbfStreaming memory for dense textbfPOint textbfTracking and online video processing.
  arXiv  Detail & Related papers  (2025-03-09T06:16:49Z)
• Exploring Temporally-Aware Features for Point Tracking [58.63091479730935]
  Chrono is a feature backbone specifically designed for point tracking with built-in temporal awareness. Chrono achieves state-of-the-art performance in a refiner-free setting on the TAP-Vid-DAVIS and TAP-Vid-Kinetics datasets.
  arXiv  Detail & Related papers  (2025-01-21T15:39:40Z)
• Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry [7.517597541959445]
  We introduce S-Temporal Visual Odometry (STVO), a novel deep network architecture to enhance accuracy and consistency of multi-frame flow matching. Our STVO achieves state-the-art performance on ETH3D benchmark and 38.9% on KITTI Odometry benchmark over the previous best methods.
  arXiv  Detail & Related papers  (2024-12-22T08:47:13Z)
• LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry [52.131996528655094]
  We present the Long-term Effective Any Point Tracking (LEAP) module. LEAP innovatively combines visual, inter-track, and temporal cues with mindfully selected anchors for dynamic track estimation. Based on these traits, we develop LEAP-VO, a robust visual odometry system adept at handling occlusions and dynamic scenes.
  arXiv  Detail & Related papers  (2024-01-03T18:57:27Z)
• Efficient Long-Short Temporal Attention Network for Unsupervised Video Object Segmentation [23.645412918420906]
  Unsupervised Video Object (VOS) aims at identifying the contours of primary foreground objects in videos without any prior knowledge. Previous methods do not fully use spatial-temporal context and fail to tackle this challenging task in real-time. This motivates us to develop an efficient Long-Short Temporal Attention network (termed LSTA) for unsupervised VOS task from a holistic view.
  arXiv  Detail & Related papers  (2023-09-21T01:09:46Z)
• DVIS: Decoupled Video Instance Segmentation Framework [15.571072365208872]
  Video instance segmentation (VIS) is a critical task with diverse applications, including autonomous driving and video editing. Existing methods often underperform on complex and long videos in real world, primarily due to two factors. We propose a decoupling strategy for VIS by dividing it into three independent sub-tasks: segmentation, tracking, and refinement.
  arXiv  Detail & Related papers  (2023-06-06T05:24:15Z)
• You Can Ground Earlier than See: An Effective and Efficient Pipeline for Temporal Sentence Grounding in Compressed Videos [56.676761067861236]
  Given an untrimmed video, temporal sentence grounding aims to locate a target moment semantically according to a sentence query. Previous respectable works have made decent success, but they only focus on high-level visual features extracted from decoded frames. We propose a new setting, compressed-domain TSG, which directly utilizes compressed videos rather than fully-decompressed frames as the visual input.
  arXiv  Detail & Related papers  (2023-03-14T12:53:27Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Enhanced Spatio-Temporal Interaction Learning for Video Deraining: A Faster and Better Framework [93.37833982180538]
  Video deraining is an important task in computer vision as the unwanted rain hampers the visibility of videos and deteriorates the robustness of most outdoor vision systems. We present a new end-to-end deraining framework, named Enhanced Spatio-Temporal Interaction Network (ESTINet) ESTINet considerably boosts current state-of-the-art video deraining quality and speed.
  arXiv  Detail & Related papers  (2021-03-23T05:19:35Z)
• Comparison of Spatiotemporal Networks for Learning Video Related Tasks [0.0]
  Many methods for learning from sequences involve temporally processing 2D CNN features from the individual frames or directly utilizing 3D convolutions within high-performing 2D CNN architectures. This work constructs an MNIST-based video dataset with parameters controlling relevant facets of common video-related tasks: classification, ordering, and speed estimation. Models trained on this dataset are shown to differ in key ways depending on the task and their use of 2D convolutions, 3D convolutions, or convolutional LSTMs.
  arXiv  Detail & Related papers  (2020-09-15T19:57:50Z)

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.