InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models

Previous methods for scene generation either suffer from limited scales or lack geometric and appearance Generating simulatable and controllable 3D scenes is an essential consistency along generated sequences. In contrast, task for a wide spectrum of applications, including we leverage the recent advancements in scalable 3D mixed reality, robotics, and the training and testing of autonomous representation and video models to achieve large dynamic vehicles (AV) [25, 33]. In particular, the requirements scene generation that allows flexible controls through HD of AV applications have introduced new challenges maps, vehicle bounding boxes, and text descriptions. First, for 3D generative models in driving scenarios, posing the we construct a map-conditioned sparse-voxel-based 3D following key desiderata: (1) fidelity and consistency, to generative model to unleash its power for unbounded voxel ensure that the generated scenes support photo-realistic rendering world generation. Then, we re-purpose a video model and while preserving consistent appearance and geometry ground it on the voxel world through a set of carefully designed for reliable and stable physics simulation; (2) largescale, pixel-aligned guidance buffers, synthesizing a consistent to generate scenes at map-level for traffic simulation; appearance. Finally, we propose a fast feed-forward and (3) controllability, to allow easy manipulation of the approach that employs both voxel and pixel branches to lift scene layout, appearance, and ego-car behaviors for curating the dynamic videos to dynamic 3D Gaussians with control-adversarial scenarios.