  This work introduces Video Diffusion Transformer (VDT), which pioneers the
use of transformers in diffusion-based video generation. It features
transformer blocks with modularized temporal and spatial attention modules to
leverage the rich spatial-temporal representation inherited in transformers. We
also propose a unified spatial-temporal mask modeling mechanism, seamlessly
integrated with the model, to cater to diverse video generation scenarios. VDT
offers several appealing benefits. 1) It excels at capturing temporal
dependencies to produce temporally consistent video frames and even simulate
the physics and dynamics of 3D objects over time. 2) It facilitates flexible
conditioning information, \eg, simple concatenation in the token space,
effectively unifying different token lengths and modalities. 3) Pairing with
our proposed spatial-temporal mask modeling mechanism, it becomes a
general-purpose video diffuser for harnessing a range of tasks, including
unconditional generation, video prediction, interpolation, animation, and
completion, etc. Extensive experiments on these tasks spanning various
scenarios, including autonomous driving, natural weather, human action, and
physics-based simulation, demonstrate the effectiveness of VDT. Additionally,
we present comprehensive studies on how \model handles conditioning information
with the mask modeling mechanism, which we believe will benefit future research
and advance the field. Project page: https:VDT-2023.github.io
