Captain Cinema: Towards Short Movie Generation
Authors: name: Junfei Xiao, name: Ceyuan Yang, name: Lvmin Zhang, name: Shengqu Cai, name: Yang Zhao, name: Yuwei Guo, name: Gordon Wetzstein, name: Maneesh Agrawala, name: Alan Yuille, name: Lu Jiang

We present Captain Cinema, a generation framework for short movie generation.
Given a detailed textual description of a movie storyline, our approach firstly
generates a sequence of keyframes that outline the entire narrative, which
ensures long-range coherence in both the storyline and visual appearance (e.g.,
scenes and characters). We refer to this step as top-down keyframe planning.
These keyframes then serve as conditioning signals for a video synthesis model,
which supports long context learning, to produce the spatio-temporal dynamics
between them. This step is referred to as bottom-up video synthesis. To support
stable and efficient generation of multi-scene long narrative cinematic works,
we introduce an interleaved training strategy for Multimodal Diffusion
Transformers (MM-DiT), specifically adapted for long-context video data. Our
model is trained on a specially curated cinematic dataset consisting of
interleaved data pairs. Our experiments demonstrate that Captain Cinema
performs favorably in the automated creation of visually coherent and narrative
consistent short movies in high quality and efficiency. Project page:
https://thecinema.ai