Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation

TL;DR

Prompt Relay is a plug-and-play method that improves multi-event video generation by enabling precise temporal control and reducing semantic interference without modifying the underlying diffusion model.

Contribution

It introduces a penalty in cross-attention to ensure each video segment attends only to its designated prompt, enhancing temporal alignment and visual quality.

Findings

Improves text-video alignment in multi-event videos.

Reduces semantic bleeding between different events.

Enhances visual quality without additional computational cost.

Abstract

Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events in real-world videos and lack explicit mechanisms to control when semantic concepts appear, how long they persist, and the order in which multiple events occur. Such control is especially important for movie-grade video synthesis, where coherent storytelling depends on precise timing, duration, and transitions between events. When using a single paragraph-style prompt to describe a sequence of complex events, models often exhibit semantic entanglement, where concepts intended for different moments in the video bleed into one another, resulting in poor text-video alignment. To address these limitations, we propose Prompt Relay, an inference-time, plug-and-play method to enable fine-grained temporal control in multi-event video generation, requiring no architectural modifications and no additional computational overhead. Prompt Relay introduces a penalty into the cross-attention mechanism, so that each temporal segment attends only to its assigned prompt, allowing the model to represent one semantic concept at a time and thereby improving temporal prompt alignment, reducing semantic interference, and enhancing visual quality.