Apple developed STARFlow-V to challenge the dominance of diffusion models in video generation. This framework utilizes normalizing flows for end-to-end learning and native likelihood estimation. It handles spatiotemporal complexity more efficiently than previous flow-based attempts. Practitioners gain a robust alternative for causal prediction and high-fidelity video synthesis without the iterative sampling overhead of diffusion.