rent methods that use autoregression with diffusion models often struggle
because their step-by-step process naturally leads to a serious error
accumulation (drift). Also, many existing ways to make long videos focus on
single, continuous scenes, making them less useful for stories with many events
and changes. This paper introduces a new approach to solve these problems.
First, we propose a novel way to annotate datasets at the frame-level,
providing detailed text guidance needed for making complex, multi-