BridgeIV：通过测试时自回归身份传播桥接定制化图像与视频生成
BridgeIV: Bridging Customized Image and Video Generation through Test-Time Autoregressive Identity Propagation
AI总结：
针对定制化文本到视频生成中的身份一致性难题，本文提出自回归结构与纹理传播模块STPM，并结合测试时奖励优化TTRO细化细节，显著提升视频帧间一致性。
摘要：
在故事内容创作中，零样本和基于微调的定制化文本到图像（CT2I）生成都取得了显著进展。相比之下，定制化文本到视频（CT2V）生成的研究仍然相对有限。现有零样本CT2V方法存在泛化能力差的问题，而另一类工作直接将基于微调的T2I模型与时间运动模块相结合，往往会导致结构和纹理信息丢失。为弥合这一差距，我们提出了自回归结构与纹理传播模块（STPM），该模块从参考主体中提取关键结构和纹理特征，并将其自回归地注入每一帧视频以增强一致性。此外，我们引入测试时奖励优化（TTRO）方法，以进一步细化细粒度细节。定量和定性实验验证了STPM和TTRO的有效性，结果显示CLIP-I和DINO一致性指标相比基线分别提升7.8和13.1。
英文摘要：
Both zero-shot and tuning-based customized text-to-image (CT2I) generation have made significant progress for storytelling content creation. In contrast, research on customized text-to-video (CT2V) generation remains relatively limited. Existing zero-shot CT2V methods suffer from poor generalization, while another line of work directly combining tuning-based T2I models with temporal motion modules often leads to the loss of structural and texture information. To bridge this gap, we propose an autoregressive structure and texture propagation module (STPM), which extracts key structural and texture features from the reference subject and injects them autoregressively into each video frame to enhance consistency. Additionally, we introduce a test-time reward optimization (TTRO) method to further refine fine-grained details. Quantitative and qualitative experiments validate the effectiveness of STPM and TTRO, demonstrating improvements of 7.8 and 13.1 in CLIP-I and DINO consistency metrics over the baseline, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
