Moviegan [2026]

We presented MovieGAN, a generative adversarial network tailored for long-form, narrative-driven video synthesis. By introducing a hierarchical generator and a narrative-aware discriminator, we demonstrate that it is possible to generate video sequences lasting up to one minute with coherent story progression and visual consistency. Future work will focus on integrating audio generation to create a complete multimodal cinematic experience.

Generating a "movie"—a sequence with a clear narrative arc, consistent characters, and coherent motion—requires modeling long-range dependencies that exceed the capacity of standard convolutional recurrent networks. To address this, we propose . Our approach reframes video generation not as a simple sequence of frames, but as a hierarchical narrative process. By decoupling global narrative planning from local pixel synthesis, MovieGAN maintains semantic consistency over minutes of footage, a scale previously unattainable with single-stage generators. moviegan

$$ L_TC = || f_t - f_t-1 ||_2 $$