TrackMAE: Video Representation Learning via Track Mask and Predict

• URL: http://arxiv.org/abs/2603.27268v1
• Date: Sat, 28 Mar 2026 13:35:23 GMT
• Title: TrackMAE: Video Representation Learning via Track Mask and Predict
• Abstract summary: Masked video modeling (MVM) has emerged as a simple and scalable self-supervised pretraining paradigm.<n>We propose TrackMAE, a simple masked video modeling paradigm that explicitly uses motion information as a reconstruction signal.<n>We evaluate on six datasets across diverse downstream settings and find that TrackMAE consistently outperforms state-of-the-art video self-supervised learning baselines.
• Score: 53.79942817343784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Masked video modeling (MVM) has emerged as a simple and scalable self-supervised pretraining paradigm, but only encodes motion information implicitly, limiting the encoding of temporal dynamics in the learned representations. As a result, such models struggle on motion-centric tasks that require fine-grained motion awareness. To address this, we propose TrackMAE, a simple masked video modeling paradigm that explicitly uses motion information as a reconstruction signal. In TrackMAE, we use an off-the-shelf point tracker to sparsely track points in the input videos, generating motion trajectories. Furthermore, we exploit the extracted trajectories to improve random tube masking with a motion-aware masking strategy. We enhance video representations learned in both pixel and feature semantic reconstruction spaces by providing a complementary supervision signal in the form of motion targets. We evaluate on six datasets across diverse downstream settings and find that TrackMAE consistently outperforms state-of-the-art video self-supervised learning baselines, learning more discriminative and generalizable representations. Code available at https://github.com/rvandeghen/TrackMAE

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