Hierarchical Masked 3D Diffusion Model for Video Outpainting
Authors: name: Fanda Fan, name: Chaoxu Guo, name: Litong Gong, name: Biao Wang, name: Tiezheng Ge, name: Yuning Jiang, name: Chunjie Luo, name: Jianfeng Zhan

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/.