Identity-preserving text-to-video (IPT2V) generation, which aims to create high-fidelity videos with consistent human identity, has become crucial for downstream applications. However, current end-to-end frameworks suffer a critical spatial-temporal trade-off: optimizing for spatially coherent layouts of key elements (e.g., character identity preservation) often compromises instruction-compliant temporal smoothness, while prioritizing dynamic realism risks disrupting the spatial coherence of visual structures. To tackle this issue, we propose a simple yet effective spatial-temporal decoupled framework that decomposes representations into spatial features for layouts and temporal features for motion dynamics. Specifically, our paper proposes a semantic prompt optimization mechanism and stage-wise decoupled generation paradigm. The former module decouples the prompt into spatial and temporal components. Aligned with the subsequent stage-wise decoupled approach, the spatial prompts guide the text-to-image (T2I) stage to generate coherent spatial features, while the temporal prompts direct the sequential image-to-video (I2V) stage to ensure motion consistency. Experimental results validate that our approach achieves excellent spatiotemporal consistency, demonstrating outstanding performance in identity preservation, text relevance, and video quality. By leveraging this simple yet robust mechanism, our algorithm secures the runner-up position in 2025 ACM MultiMedia Challenge. Our code is available at https://github.com/rain152/IPVG.

翻译：身份保持的文本到视频（IPT2V）生成旨在创建具有一致人物身份的高保真视频，对于下游应用至关重要。然而，当前端到端框架面临关键的时空权衡：优化关键元素（如角色身份保持）的空间连贯布局通常会损害指令符合的时间平滑性，而优先考虑动态真实感则可能破坏视觉结构的空间连贯性。为解决这一问题，我们提出了一种简单而有效的时空解耦框架，将表征分解为用于布局的空间特征和用于运动动态的时间特征。具体而言，本文提出了一种语义提示优化机制和分阶段解耦生成范式。前者将提示解耦为空间和时间分量。结合后续的分阶段解耦方法，空间提示引导文本到图像（T2I）阶段生成连贯的空间特征，而时间提示则指导序列图像到视频（I2V）阶段以确保运动一致性。实验结果验证了我们的方法实现了优异的时空一致性，在身份保持、文本相关性和视频质量方面表现出色。通过利用这种简单而稳健的机制，我们的算法在2025年ACM多媒体挑战赛中获得了亚军。代码可在https://github.com/rain152/IPVG获取。