DreamWorld: Geometry-Grounded Video Diffusion for 3D-Consistent World Modeling

Camera-controlled video diffusion models (VDMs) have re-cently emerged as powerful world models, enabling users to explore 3Dscenes through flexible, user-defined camera trajectories. Nevertheless,current VDMs typically rely on implicit spatiotemporal representationswithout explicit 3D geometric grounding. Such geometry-agnostic mod-eling often leads to issues including geometrically implausible struc-tures and cross-view spatial inconsistencies. To alleviate this, we presentDreamWorld, a new recipe of world model that novelly bridges the strongspatial structure priors of 3D foundation models with the high-fidelitygenerative capabilities of video diffusion models for geometry-consistent3D scene generation. Specifically, given the input image and camera tra-jectory, DreamWorld first learns a geometry video diffusion model topredict compact geometry features for the target novel views, function-ing as explicit structure pivots to reflect the underlying 3D spatial layout.To achieve this, we introduce a distillation paradigm that transfers high-level structural knowledge from a pretrained 3D foundation model to thediffusion model, thereby enabling it to produce geometrically consistentand spatially coherent features. Conditioned on such geometry features,another appearance video diffusion model is then utilized to synthesizethe final video, ensuring improved geometric plausibility and cross-viewconsistency while maintaining high visual fidelity. Extensive experimentsdemonstrate that DreamWorld outperforms existing methods in visualquality, 3D consistency, and camera controllability. Our project page isavailable at https://yanghb22-fdu.github.io/DreamWorld.