  This paper presents a unified multimodal pre-trained model called N\"UWA that
can generate new or manipulate existing visual data (i.e., images and videos)
for various visual synthesis tasks. To cover language, image, and video at the
same time for different scenarios, a 3D transformer encoder-decoder framework
is designed, which can not only deal with videos as 3D data but also adapt to
texts and images as 1D and 2D data, respectively. A 3D Nearby Attention (3DNA)
mechanism is also proposed to consider the nature of the visual data and reduce
the computational complexity. We evaluate N\"UWA on 8 downstream tasks.
Compared to several strong baselines, N\"UWA achieves state-of-the-art results
on text-to-image generation, text-to-video generation, video prediction, etc.
Furthermore, it also shows surprisingly good zero-shot capabilities on
text-guided image and video manipulation tasks. Project repo is
https://github.com/microsoft/NUWA.
