3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation
Analysis
This paper introduces a novel Driving World Model (DWM) that leverages 3D Gaussian scene representation to improve scene understanding and multi-modal generation in driving environments. The key innovation lies in aligning textual information directly with the 3D scene by embedding linguistic features into Gaussian primitives, enabling better context and reasoning. The paper addresses limitations of existing DWMs by incorporating 3D scene understanding, multi-modal generation, and contextual enrichment. The use of a task-aware language-guided sampling strategy and a dual-condition multi-modal generation model further enhances the framework's capabilities. The authors validate their approach with state-of-the-art results on nuScenes and NuInteract datasets, and plan to release their code, making it a valuable contribution to the field.
Key Takeaways
- •Proposes a novel DWM based on 3D Gaussian scene representation.
- •Enables both 3D scene understanding and multi-modal scene generation.
- •Achieves early modality alignment by embedding linguistic features into Gaussian primitives.
- •Employs a task-aware language-guided sampling strategy.
- •Utilizes a dual-condition multi-modal generation model.
- •Achieves state-of-the-art performance on nuScenes and NuInteract datasets.
- •Code will be released publicly.
“Our approach directly aligns textual information with the 3D scene by embedding rich linguistic features into each Gaussian primitive, thereby achieving early modality alignment.”