MV2MAE: Multi-View Video Masked Autoencoders
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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
- 2019NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understandingcited by 533
- 2023VideoMAE V2: Scaling Video Masked Autoencoders with Dual Maskingcited by 528
- 2022Masked Autoencoders As Spatiotemporal Learnerscited by 429
- 2020Spatiotemporal Contrastive Video Representation Learningcited by 372
- 2020Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognitioncited by 343
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- 2017PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understandingcited by 101
- 2019Self-supervised Spatio-temporal Representation Learning for Videos by Predicting Motion and Appearance Statisticscited by 92
- 2020MS2L: Multi-Task Self-Supervised Learning for Skeleton Based Action Recognitioncited by 92
- 2022Masked Video Distillation: Rethinking Masked Feature Modeling for Self-supervised Video Representation Learningcited by 90
- 2022Self-Supervised Learning for Videos: A Surveycited by 85
- 2023Multi-View Masked World Models for Visual Robotic Manipulationcited by 80
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- 2018Self-Supervised Spatiotemporal Feature Learning via Video Rotation Predictioncited by 71
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- 2020VPN: Learning Video-Pose Embedding for Activities of Daily Livingcited by 57
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- 2022Masked Motion Encoding for Self-Supervised Video Representation Learningcited by 39
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- 2018Unsupervised Learning of View-invariant Action Representationscited by 32
- 2019DMCL: Distillation Multiple Choice Learning for Multimodal Action Recognitioncited by 30
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Citing papers per year: 2025: 5; 2026: 1.
6 papers.
- 2025MuM: Multi-View Masked Image Modeling for 3D Visioncited by 5
- 2025Content-Distortion High-Order Interaction for Blind Image Quality Assessmentcited by 2
- 2025VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imagingcited by 1
- 2025Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Surveycited by 1
- 2025Towards Generalizing Temporal Action Segmentation to Unseen Viewscited by 0
- 2026SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Trainingcited by 0
- 2026SHANDS: A Multi-View Dataset and Benchmark for Surgical Hand-Gesture and Error Recognition Toward Medical Training27 Mar 2026
- 2025MuM: Multi-View Masked Image Modeling for 3D Vision21 Nov 2025
- 2025Multi Camera Connected Vision System with Multi View Analytics: A Comprehensive Survey10 Oct 2025
- 2025VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging21 May 2025
- 2025Content-Distortion High-Order Interaction for Blind Image Quality Assessment7 Apr 2025
- 2025Towards Generalizing Temporal Action Segmentation to Unseen Views3 Apr 2025