HuggingFace Daily Papers·· 1 天前
4LC:面向动态世界建模高效条件生成的 4D 潜缓存
4LC: 4D Latent Cache for Efficient Conditioning in Dynamic World Modeling
AI 导读
4LC 是一种 4D 潜缓存,通过直接复用静态与动态内容的视觉表示来提供生成条件,避免了逐请求的 RGB 渲染与重编码。它绑定 VAE、DINO、V-JEPA、VGGT 等多个视觉编码器的表示到共享的几何与运动,并提供可按时间与视角独立读取的时空接口。基于 4LC 构建的 4LC-Video 用单一模型、无需任务特定微调即支持相机控制、时间控制、新视角合成、场景重访与长期生成。
正文
Abstract:For video generation in dynamic world modeling, observed static and dynamic content should be efficiently reused to provide conditions for repeated generation at different times and viewpoints. We introduce 4LC, a 4D latent cache with composite visual representations and a flexible spatiotemporal access interface. As a latent cache, 4LC directly reuses visual representations of static and dynamic content to provide generation conditions, avoiding per-request RGB rendering and re-encoding. As a composite cache, 4LC binds visual representations from multiple visual encoders (e.g., VAE, DINO, V-JEPA, VGGT) to shared geometry and motion, allowing the composite representations to describe the dynamic world with rich information. Besides, we design a flexible spatiotemporal interface, which directly reads the cache at independently specified times and viewpoints and combines readouts across observations, providing a common conditioning format for diverse downstream tasks. Building on 4LC, we develop 4LC-Video to address multiple video generation tasks through different configurations of source observations, spatiotemporal requests, and historical reuse, including camera control, temporal control, novel-view synthesis, scene revisits, and long-term generation. Our experiments show more efficient condition acquisition, effective spatiotemporal controllability, and scene consistency across tasks using a single 4LC-Video model without task-specific fine-tuning.
| Comments: | Project page: this https URL |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.17625 [cs.CV] |
| (or arXiv:2610.17625v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.17625 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jianhui Zhang [view email]
[v1]
Tue, 13 Oct 2026 02:47:58 UTC (11,757 KB)
来源:HuggingFace Daily Papers · arxiv.org