https://vilab.team/author/%E6%9D%8E%E8%94%9A/ 李蔚 Hugo Blox Builder (https://hugoblox.com)en-usMon, 20 Apr 2026 00:00:00 +0000 https://vilab.team/author/%E6%9D%8E%E8%94%9A/avatar_hu509644536962498473.jpg https://vilab.team/author/%E6%9D%8E%E8%94%9A/ https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/ Mon, 20 Apr 2026 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/ <p>热烈祝贺李蔚同学！论文《ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation》已发表在 <em>ICLR 2026</em>。</p> <h2 id="reactid-synchronizing-realistic-actions-and-identity-in-personalized-video-generation">ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation</h2> <p><strong>祝贺李蔚同学！该论文已发表在 <em>ICLR</em>。</strong></p> <p> <figure > <div class="d-flex justify-content-center"> <div class="w-100" ><img alt="ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation" srcset=" /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/images/paper-01_hu10185920340004675066.webp 400w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/images/paper-01_hu15268550746155155384.webp 760w, /event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/images/paper-01_hu4770338007721801293.webp 1200w" src="https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-iclr/images/paper-01_hu10185920340004675066.webp" width="760" height="288" loading="lazy" data-zoomable /></div> </div></figure> </p> <ul> <li><strong>作者：</strong> Wei Li、Yiheng Zhang、Fuchen Long、Zhaofan Qiu、Ting Yao、Xiaoyan Sun、Tao Mei</li> <li><strong>发表载体：</strong> <em>ICLR</em></li> <li><strong>发表时间：</strong> 2026年4月20日</li> <li><strong>相关链接：</strong> <a href="https://proceedings.iclr.cc/paper_files/paper/2026/hash/6600458132d025c68a01e82081597b32-Abstract-Conference.html" target="_blank" rel="noopener">论文链接</a> · <a href="https://proceedings.iclr.cc/paper_files/paper/2026/file/6600458132d025c68a01e82081597b32-Paper-Conference.pdf" target="_blank" rel="noopener">PDF</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>本文提出ReactID框架，旨在协调个性化视频生成中身份一致性与动作真实性的矛盾。针对主体-视频对齐不精确、训练不稳定、细粒度动作建模不足三大挑战，从数据、训练和动作建模三方面协同改进：构建高精度标注的ReactID-Data数据集；设计由易到难的渐进式训练课程；提出基于时间线的条件机制，通过主体感知交叉注意力和时间自适应RoPE，将子动作与特定主体绑定并嵌入时间坐标，从而生成更自然、可控的视频。</p> https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-2025-ieee-international-conference-on-multimedi/ Mon, 30 Jun 2025 00:00:00 +0000 https://vilab.team/event/%E7%A5%9D%E8%B4%BA%E5%AE%9E%E9%AA%8C%E5%AE%A4%E7%A7%91%E7%A0%94%E6%88%90%E6%9E%9C%E5%8F%91%E8%A1%A8%E4%BA%8E-2025-ieee-international-conference-on-multimedi/ <p>热烈祝贺李蔚同学！论文《Create anything anywhere: Layout-controllable personalized diffusion model for multiple subjects》已发表在 ICME 2025。</p> <h2 id="create-anything-anywhere-layout-controllable-personalized-diffusion-model-for-multiple-subjects">Create anything anywhere: Layout-controllable personalized diffusion model for multiple subjects</h2> <p><strong>祝贺李蔚同学！该论文已发表在 2025 IEEE International Conference on Multimedia and Expo (ICME), 1-6。</strong></p> <ul> <li><strong>作者：</strong> Wei Li、Hebei Li、Yansong Peng、Siying Wu、Yueyi Zhang、Xiaoyan Sun</li> <li><strong>发表载体：</strong> 2025 IEEE International Conference on Multimedia and Expo (ICME), 1-6</li> <li><strong>发表时间：</strong> 2025年6月30日</li> <li><strong>相关链接：</strong> <a href="https://ieeexplore.ieee.org/abstract/document/11209272/" target="_blank" rel="noopener">论文链接</a> · <a href="https://arxiv.org/pdf/2505.20909" target="_blank" rel="noopener">PDF</a></li> </ul> <h3 id="论文介绍">论文介绍</h3> <p>Diffusion models have significantly advanced text-to-image generation, laying the foundation for the development of personalized generative frameworks. However, existing methods lack precise layout controllability and overlook the potential of dynamic features of reference subjects in improving fidelity. In this work, we propose Layout-Controllable Personalized Diffusion (LCP-Diffusion) model, a novel framework that integrates subject identity preservation with flexible layout guidance in a tuning-free approach. Our model employs a Dynamic-Static Complementary Visual Refining module to comprehensively capture the intricate details of reference subjects, and introduces a Dual Layout Control mechanism to enforce robust spatial control across both training and inference stages. Extensive experiments validate that LCP-Diffusion excels in both identity preservation and layout controllability. To the best of our …</p>