Part 4/11:
One of the most crucial innovations is the use of flow matching during training. This technique allows the model to predict how scenes should evolve over time, maintaining both visual coherence and temporal consistency. It ensures that objects move naturally, and scenes develop smoothly, even across extended durations.
Additionally, MovieGenen leverages compressed latent space processing, driven by a Temporal Autoencoder (TAE). This approach compresses video data across spatial and temporal dimensions, reducing the computational load, and enabling the system to produce 1080p videos at 16 FPS — a practical standard for many applications, although slightly below the cinematic 24 FPS.