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.

翻译：多视角拍摄的视频有助于感知世界的三维结构，并促进动作识别、跟踪等计算机视觉任务。本文提出一种基于同步多视角视频的自监督学习方法，通过跨视角重建任务向模型中注入几何信息。我们的方法基于掩膜自编码器（MAE）框架，除同视角解码器外，额外引入独立的跨视角解码器，利用交叉注意力机制从源视角视频重建目标视角视频，以增强模型对视角变化的鲁棒性。针对视频中静态区域易被平凡重建而阻碍有意义的表征学习的问题，我们提出运动加权重建损失以改进时序建模。实验结果表明，本方法在NTU-60、NTU-120和ETRI数据集上取得了最优结果，且在NUCLA、PKU-MMD-II和ROCOG-v2数据集的迁移学习设置中展现了强鲁棒性。代码将开源。