We provide the following contributions in this work:

• A simple method for training a Large Language Model
(LLM) speciﬁcally for video generation tasks, utilizing
tokenized video and audio data that seamlessly incorpo-
rates both text-paired and unpaired video data.

• An approach to super-resolution that increases video res-
olution within the latent token space using a bidirectional
transformer with efﬁcient windowed local attention.

• Evaluations and demonstrations that showcase the LLM’s
competitive performance, especially in producing realis-
tic and interesting motion.

2. Related Work
Video Diffusion Models Most video generation works
use diffusion-based methods for text-to-video [4, 5, 24,
29, 32, 55, 66, 68, 69, 78, 79, 79, 83] and video-to-video
VideoPoet