Abstract
Generating large-scale, high-fidelity 3D environments is a critical requirement for the development and testing of autonomous driving systems. While recent generative models have made strides in temporal video consistency, they often struggle with spatial expansion and precise geometric control. This paper introduces X-Scene, a novel framework that synthesizes spatially coherent, "infinite" 3D driving scenes. By integrating LLM-enriched text prompts with a unified occupancy-image-video diffusion pipeline, X-Scene produces high-fidelity environments that can be lifted into 3D Gaussian Splatting (3DGS) representations for realistic, closed-loop simulation.
1. Introduction
In the field of autonomous driving, the ability to simulate complex, large-scale environments is essential for safety validation. Current generative AI models are proficient at creating short video clips, but building expansive 3D worlds remains a significant challenge. Most existing methods prioritize temporal consistency (ensuring smooth transitions between frames) at the expense of spatial logic, often leading to environments that lack structural integrity over long distances. Furthermore, these models frequently struggle to balance high-level user intent with low-level geometric precision.
X-Scene addresses these gaps by adopting a "geometry-first" hierarchical approach. It decouples the generation of the scene's structural "skeleton" from its visual "skin," allowing for the creation of vast, detailed, and controllable 3D environments.
2. The X-Scene Framework
X-Scene introduces several key innovations to achieve high-fidelity scene synthesis and flexible controllability:
2.1 Multi-Granular Controllability
To accommodate different user needs, X-Scene employs a dual-mode control scheme:
- High-Level Text Control: Users can provide simple prompts like "a busy urban intersection." A Retrieval-Augmented Generation (RAG) pipeline, powered by a Large Language Model (LLM), enriches these prompts into detailed scene graphs based on real-world patterns stored in a structured memory bank.
- Low-Level Geometric Control: For precise scenario engineering, the system supports Bird’s-Eye View (BEV) layouts, allowing users to define exact road geometries and object placements.
2.2 Triplane Diffusion with Deformable Attention
Efficiently representing 3D space is a core challenge. X-Scene utilizes a "triplane" representation—three orthogonal 2D planes that describe the 3D volume. To preserve fine geometric details during the diffusion process, the authors introduced a triplane deformable attention mechanism. This mechanism allows the model to aggregate features more effectively across the planes, preventing the loss of structural resolution common in standard diffusion models.
2.3 Unified Generation Pipeline
The synthesis process follows a logical sequence to ensure consistency:
- 3D Semantic Occupancy: The model first generates the 3D "skeleton" of the scene.
- Multi-view Image Generation: Using rendered depth and semantic maps as guides, the model generates the visual textures ("skin") across multiple camera views.
- Motion-Aware Diffusion: A specialized module ensures that dynamic elements remain temporally coherent across video frames.
- 3DGS Lifting: Finally, the generated data is converted into 3D Gaussian Splatting (3DGS) representations, creating a renderable, interactive 3D environment.
2.4 Consistency-Aware Outpainting
To facilitate "infinite" scene generation, X-Scene uses a sliding-window approach. By conditioning the denoising process on the overlapping regions of previously generated segments, the model ensures that the environment remains seamless as it expands spatially.
3. Experimental Evaluation
3.1 Datasets
The framework was trained and evaluated using the Occ3D-nuScenes and nuScenes datasets. These provided the necessary 3D occupancy data, multi-view imagery, and HD map annotations. Additionally, a Scene Description Memory Bank was derived from nuScenes to support the RAG-based text enrichment process.
3.2 Key Results
X-Scene demonstrates state-of-the-art performance across several benchmarks:
- Occupancy Fidelity: The model achieved a 92.4 mIoU for occupancy reconstruction, a significant leap over the previous baseline, UniScene (73.7).
- Visual Realism: The model recorded a Fréchet Inception Distance (FID) of 11.29, representing a 4.9% improvement in image quality over existing methods.
- Downstream Utility: Data generated by X-Scene was shown to be highly effective for training. Fine-tuning an occupancy prediction model with X-Scene's synthetic data resulted in a +4.9% IoU and +6.8% mIoU improvement in performance.
- Efficiency: Despite its complexity, the system is efficient, generating a single scene chunk in approximately 6 seconds on an NVIDIA RTX A6000 GPU.
4. Discussion and Future Work
X-Scene represents a major step forward in 3D scene generation by anchoring visual fidelity in geometric consistency. The use of LLMs as "scene architects" allows for intuitive yet realistic environment design. However, some limitations remain. Temporal drift can still occur in very long-duration videos, and the current scene memory is primarily constrained to the geographic and environmental diversity found in the nuScenes dataset. Future research will focus on expanding the model's vocabulary to include more diverse global environments and improving multi-agent reactive behaviors for more robust closed-loop testing.