Reason the Scene, Reconstruct the Space, Render the Unseen: TinkerCube for Spatial Intelligence

Spatial reasoning, 3D reconstruction, and novel-view synthesis describe complementary aspects of the physical world, yet are typically isolated, limiting exchange between semantic and geometric evidence. We present TinkerCube, a coordinated multimodal model connecting spatial understanding, target-view geometry prediction, and controllable visual generation. TinkerCube augments a Mixture-of-Transformer-Experts backbone with a multi-view geometry pathway and role-specific interfaces that align 2D appearance, camera states, and cross-view structure in a shared context. Geometry-aware reasoning supplies explicit spatial evidence to the language expert; language-conditioned geometry converts motion and content instructions into target cameras, depths, and point maps; render-guided generation projects a reference-only point cloud into the target view to constrain completion. Three-stage training progressively aligns spatial tokens with language, learns motion-conditioned target geometry, and adapts coverage-aware view synthesis while preserving earlier capabilities. Across spatial question answering, depth and camera estimation, point-map reconstruction, and novel-view synthesis, TinkerCube delivers consistent performance, with leading results on major spatial-reasoning benchmarks and strong view-synthesis performance on DL3DV and RealEstate10K. These results show that integrating semantic reasoning, 3D geometry, and visual generation through joint training and interaction effectively strengthens spatial intelligence.