Mask^2DiT: Dual Mask-based Diffusion Transformer for Multi-Scene Long
  Video Generation
Authors: name: Tianhao Qi, name: Jianlong Yuan, name: Wanquan Feng, name: Shancheng Fang, name: Jiawei Liu, name: SiYu Zhou, name: Qian He, name: Hongtao Xie, name: Yongdong Zhang

Sora has unveiled the immense potential of the Diffusion Transformer (DiT)
architecture in single-scene video generation. However, the more challenging
task of multi-scene video generation, which offers broader applications,
remains relatively underexplored. To bridge this gap, we propose Mask^2DiT, a
novel approach that establishes fine-grained, one-to-one alignment between
video segments and their corresponding text annotations. Specifically, we
introduce a symmetric binary mask at each attention layer within the DiT
architecture, ensuring that each text annotation applies exclusively to its
respective video segment while preserving temporal coherence across visual
tokens. This attention mechanism enables precise segment-level
textual-to-visual alignment, allowing the DiT architecture to effectively
handle video generation tasks with a fixed number of scenes. To further equip
the DiT architecture with the ability to generate additional scenes based on
existing ones, we incorporate a segment-level conditional mask, which
conditions each newly generated segment on the preceding video segments,
thereby enabling auto-regressive scene extension. Both qualitative and
quantitative experiments confirm that Mask^2DiT excels in maintaining visual
consistency across segments while ensuring semantic alignment between each
segment and its corresponding text description. Our project page is
https://tianhao-qi.github.io/Mask2DiTProject.