Video outpainting aims to adequately complete missing areas at the edges of video frames. Compared to image outpainting, it presents an additional challenge as the model should maintain the temporal consistency of the filled area. In this paper, we introduce a masked 3D diffusion model for video outpainting. We use the technique of mask modeling to train the 3D diffusion model. This allows us to use multiple guide frames to connect the results of multiple video clip inferences, thus ensuring temporal consistency and reducing jitter between adjacent frames. Meanwhile, we extract the global frames of the video as prompts and guide the model to obtain information other than the current video clip using cross-attention. We also introduce a hybrid coarse-to-fine inference pipeline to alleviate the artifact accumulation problem. The existing coarse-to-fine pipeline only uses the infilling strategy, which brings degradation because the time interval of the sparse frames is too large. Our pipeline benefits from bidirectional learning of the mask modeling and thus can employ a hybrid strategy of infilling and interpolation when generating sparse frames. Experiments show that our method achieves state-of-the-art results in video outpainting tasks. More results are provided at our https://fanfanda.github.io/M3DDM/.

翻译：视频外扩旨在充分补全视频帧边缘的缺失区域。与图像外扩相比，该任务面临额外挑战：模型需保持填充区域的时序一致性。本文提出一种掩码3D扩散模型用于视频外扩。我们采用掩码建模技术训练该3D扩散模型，从而能够利用多帧引导图像连接多个视频片段推理结果，在保证时序一致性的同时减少相邻帧间的抖动。同时，我们提取视频全局帧作为提示，通过交叉注意力机制引导模型获取当前视频片段之外的全局信息。为缓解伪影累积问题，我们还引入混合粗到细推理管线。现有粗到细管线仅采用内插策略，因稀疏帧时间间隔过大导致性能退化。得益于掩码建模的双向学习能力，本管线在生成稀疏帧时可混合使用内插与插值策略。实验表明，该方法在视频外扩任务中达到最优水平。更多结果详见我们的项目主页：https://fanfanda.github.io/M3DDM/。