世界模型杀入SLAM：用视频生成预测未来帧，实现主动定位与遮挡重建

TL;DR - A WeChat roundup of recent efforts to fuse video-generation "world models" with SLAM, so localization and mapping shift from passive perception to predicting future frames and hallucinating occluded regions. It matters because it points at a practical robustness path for embodied AI in dynamic or occluded environments, though the post is a vendor-style survey that ends in a course/community promotion.

• Xiaomi Auto World Model couples 3D reconstruction with video generation to produce future frames, unobserved viewpoints and occluded content, cited at 0.19s per frame and up to 81 consecutive frames.
• Dream-SLAM adds a "dreaming" mechanism: a "retrospective dream" aligns historical and current observations so dynamic objects become localization cues, while a "foresight dream" imagines unobserved structure; claimed >30% shorter exploration paths in simulation.
• Gravity 4D WAM (Geek+) moves from pixel prediction to 4D latent-space modeling of appearance, point-cloud structure and motion, reported to lift LIBERO-Plus success from 73.73% to 78.62%; Shanghai AI Lab's Aether jointly optimizes 4D reconstruction, video prediction and visual planning with synthetic-to-real transfer.
• A Stereo World Model converts monocular diffusion into binocular generation for left/right-consistent stereo video; all figures are as-claimed in the post, with no independent benchmarks or citations given.