Wei Li | ViLab

Holo-World: Unified Camera, Object and Weather Control for Video World Model

Thu, 18 Jun 2026 00:00:00 +0000

本文提出Holo-World，一种统一的视频世界模型，可从单张图像出发，联合控制相机运动、物体动态和天气状态。作者构建了HoloStateData数据集，将多样视频转换为统一控制样本；并提出统一场景适配器，将世界保持与天气迁移分解到不同参数子空间，利用渲染背景、几何缓冲和物体控制维持场景结构，同时建模天气相关外观与粒子效果。场景-天气分解CFG进一步分别引导场景和天气残差，增强目标天气效果。实验表明，Holo-World在保持精确控制的同时，实现了优于视频到视频基线的天气状态生成。

ReactID: Synchronizing Realistic Actions and Identity in Personalized Video Generation

Mon, 20 Apr 2026 00:00:00 +0000

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。

Efficient spiking point mamba for point cloud analysis

Sun, 19 Oct 2025 00:00:00 +0000

本文提出 Spiking Point Mamba (SPM)，这是首个将 Mamba 引入三维点云分析的脉冲神经网络。针对直接适配 Mamba 时存在的时序动态不匹配和脉冲引起的信息损失问题，作者设计了层次动态编码 (HDE) 以增强动态时序建模，并提出 Spiking Mamba Block (SMB) 来学习跨时间步特征并减少脉冲信息丢失。此外，采用非对称 SNN 训练策略进一步提升性能。SPM 可作为高效骨干网络，适用于点云分类、部件分割与重建等任务。

Create anything anywhere: Layout-controllable personalized diffusion model for multiple subjects

Mon, 30 Jun 2025 00:00:00 +0000

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 …