MarDini: Masked Autoregressive Diffusion for Video Generation at Scale
Authors: name: Haozhe Liu, name: Shikun Liu, name: Zijian Zhou, name: Mengmeng Xu, name: Yanping Xie, name: Xiao Han, name: Juan C. Pérez, name: Ding Liu, name: Kumara Kahatapitiya, name: Menglin Jia, name: Jui-Chieh Wu, name: Sen He, name: Tao Xiang, name: Jürgen Schmidhuber, name: Juan-Manuel Pérez-Rúa

We introduce MarDini, a new family of video diffusion models that integrate
the advantages of masked auto-regression (MAR) into a unified diffusion model
(DM) framework. Here, MAR handles temporal planning, while DM focuses on
spatial generation in an asymmetric network design: i) a MAR-based planning
model containing most of the parameters generates planning signals for each
masked frame using low-resolution input; ii) a lightweight generation model
uses these signals to produce high-resolution frames via diffusion de-noising.
MarDini's MAR enables video generation conditioned on any number of masked
frames at any frame positions: a single model can handle video interpolation
(e.g., masking middle frames), image-to-video generation (e.g., masking from
the second frame onward), and video expansion (e.g., masking half the frames).
The efficient design allocates most of the computational resources to the
low-resolution planning model, making computationally expensive but important
spatio-temporal attention feasible at scale. MarDini sets a new
state-of-the-art for video interpolation; meanwhile, within few inference
steps, it efficiently generates videos on par with those of much more expensive
advanced image-to-video models.