mlsanityData to 14 Oct 2026

MV2MAE: Multi-View Video Masked Autoencoders

arXiv 2401.15900 ↗First posted 29 Jan 2024cs.CVcited by 6

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Abstract (arXiv)

Videos captured from multiple viewpoints can help in perceiving the 3D structure of the world and benefit computer vision tasks such as action recognition, tracking, etc. In this paper, we present a method for self-supervised learning from synchronized multi-view videos. We use a cross-view reconstruction task to inject geometry information in the model. Our approach is based on the masked autoencoder (MAE) framework. In addition to the same-view decoder, we introduce a separate cross-view decoder which leverages cross-attention mechanism to reconstruct a target viewpoint video using a video from source viewpoint, to help representations robust to viewpoint changes. For videos, static regions can be reconstructed trivially which hinders learning meaningful representations. To tackle this, we introduce a motion-weighted reconstruction loss which improves temporal modeling. We report state-of-the-art results on the NTU-60, NTU-120 and ETRI datasets, as well as in the transfer learning setting on NUCLA, PKU-MMD-II and ROCOG-v2 datasets, demonstrating the robustness of our approach. Code will be made available.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 51 of its 71 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

51 of 71 references.

References · 51

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38 more
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Citing papers per year: 2025: 5; 2026: 1.

6 papers.