CoTracker3: Simpler and Better Point Tracking by Pseudo-Labelling Real Videos
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Abstract (arXiv)
Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of annotating real videos for this task. However, this can result in suboptimal performance due to the statistical gap between synthetic and real videos. In order to understand these issues better, we introduce CoTracker3, comprising a new tracking model and a new semi-supervised training recipe. This allows real videos without annotations to be used during training by generating pseudo-labels using off-the-shelf teachers. The new model eliminates or simplifies components from previous trackers, resulting in a simpler and often smaller architecture. This training scheme is much simpler than prior work and achieves better results using 1,000 times less data. We further study the scaling behaviour to understand the impact of using more real unsupervised data in point tracking. The model is available in online and offline variants and reliably tracks visible and occluded points.
Key facts. Cited by 122 papers in the map (data through 7 Oct 2026). Builds on 28 of its 41 references that are in the map. First posted on arXiv 15 Oct 2024.
Builds on
28 of 41 references.
References · 28
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2017SuperPoint: Self-Supervised Interest Point Detection and Descriptioncited by 1,313
- 2023CoTracker: It is Better to Track Togethercited by 516
- 2022Kubric: A scalable dataset generatorcited by 373
- 2020DISK: Learning local features with policy gradientcited by 310
- 2023TAPIR: Tracking Any Point with per-frame Initialization and temporal Refinementcited by 281
- 2023PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Trackingcited by 274
- 2019Learning Correspondence from the Cycle-Consistency of Timecited by 269
- 2022TAP-Vid: A Benchmark for Tracking Any Point in a Videocited by 258
- 2020Space-Time Correspondence as a Contrastive Random Walkcited by 239
16 more
- 2023Visual Geometry Grounded Deep Structure From Motioncited by 225
- 2022Particle Video Revisited: Tracking Through Occlusions Using Point Trajectoriescited by 215
- 2017UnFlow: Unsupervised Learning of Optical Flow with a Bidirectional Census Losscited by 202
- 2018Tracking Emerges by Colorizing Videoscited by 166
- 2023DynamicStereo: Consistent Dynamic Depth from Stereo Videoscited by 147
- 2019SelFlow: Self-Supervised Learning of Optical Flowcited by 128
- 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
- 2024BootsTAP: Bootstrapped Training for Tracking-Any-Pointcited by 115
- 2020MAST: A Memory-Augmented Self-supervised Trackercited by 109
- 2017Occlusion Aware Unsupervised Learning of Optical Flowcited by 101
- 2024Local All-Pair Correspondence for Point Trackingcited by 77
- 2019DDFlow: Learning Optical Flow with Unlabeled Data Distillationcited by 63
- 2023Dense Optical Tracking: Connecting the Dotscited by 58
- 2014Unsupervised Learning of Spatiotemporally Coherent Metricscited by 45
- 2024TAPTR: Tracking Any Point with Transformers as Detectioncited by 43
- 2024Refining Pre-Trained Motion Modelscited by 12
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Citing papers per year: 2024: 1; 2025: 24; 2026: 97.
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