标题： 无需训练的提示、图像和引导增强的身份保留文本到视频生成 显示英文标题

标题： Identity-Preserving Text-to-Video Generation via Training-Free Prompt, Image, and Guidance Enhancement

摘要： 保持身份的文本到视频（IPT2V）生成创建与参考主体图像和文本提示都一致的视频。 虽然在ID匹配数据上微调大型预训练视频扩散模型在IPT2V上取得了最先进的结果，但数据稀缺性和高昂的调优成本阻碍了更广泛的改进。 因此，我们引入了一个无需训练的提示、图像和引导增强（TPIGE）框架，弥合视频描述与参考图像之间的语义差距，并设计了采样引导，以增强身份保留和视频质量，在最小成本下实现性能提升。具体来说，我们首先提出了面向面部的提示增强，使用GPT-4o通过参考图像获得的面部细节来增强文本提示。 然后我们提出了面向提示的参考图像增强，利用保持身份的图像生成器来优化参考图像，修正与文本提示的冲突。 上述相互优化显著提高了视频生成前的输入质量。 最后，我们提出了面向身份的时空引导增强，利用统一梯度在生成过程中共同优化身份保留和视频质量。我们的方法优于之前的工作，并通过1000视频测试集上的自动和人工评估得到了验证，在ACM多媒体2025身份保留视频生成挑战赛中获得第一名，展示了最先进的性能和强大的泛化能力。 代码可在https://github.com/Andyplus1/IPT2V.git获取。 显示英文摘要

摘要： Identity-preserving text-to-video (IPT2V) generation creates videos faithful to both a reference subject image and a text prompt. While fine-tuning large pretrained video diffusion models on ID-matched data achieves state-of-the-art results on IPT2V, data scarcity and high tuning costs hinder broader improvement. We thus introduce a Training-Free Prompt, Image, and Guidance Enhancement (TPIGE) framework that bridges the semantic gap between the video description and the reference image and design sampling guidance that enhances identity preservation and video quality, achieving performance gains at minimal cost.Specifically, we first propose Face Aware Prompt Enhancement, using GPT-4o to enhance the text prompt with facial details derived from the reference image. We then propose Prompt Aware Reference Image Enhancement, leveraging an identity-preserving image generator to refine the reference image, rectifying conflicts with the text prompt. The above mutual refinement significantly improves input quality before video generation. Finally, we propose ID-Aware Spatiotemporal Guidance Enhancement, utilizing unified gradients to optimize identity preservation and video quality jointly during generation.Our method outperforms prior work and is validated by automatic and human evaluations on a 1000 video test set, winning first place in the ACM Multimedia 2025 Identity-Preserving Video Generation Challenge, demonstrating state-of-the-art performance and strong generality. The code is available at https://github.com/Andyplus1/IPT2V.git.