TrackMAE: Video Representation Learning via Track Mask and Predict

Masked video modeling (MVM) has emerged as a simple and scalable self-supervised pretraining paradigm, but only models 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 the random tube masking with a motion-aware masking strategy. We enhance video representations learned in both pixel and feature semantic reconstruction space by providing a complimentary supervision signal in the form of motion targets. We evaluate on six datasets across diverse downstream settings and find that TrackMAE consistently outperforms the state-of-the-art video SSL baselines, therefore learning more discriminative and generalizable representations.