← 研踪 PaperTrailCVF · CVPR 2026:cvf:3ac980a453a9ae34b2bd07d690fe7f40 · 记录 #1
Grounded Latents for Entity-Centric 4D Scene Generation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 03:08
摘要
Although recent work has explored generative modeling of 3D or 4D driving scenes, most approaches operate on dense voxel-based representations, which are computationally expensive and struggle to maintain temporal or structural consistency. These methods often produce blurred or merged entities (i.e., cars, trucks, pedestrians) and lack fine-grained control over individual scene elements. We propose LatentWorld, a framework for generative modeling in a compact, entity-centric latent space, where each grounded 3D latent represents a semantically meaningful local region of the scene. This formulation enables precise, consistent control of both foreground and background elements while preserving geometric detail. We further extend this representation to 4D by learning a motion diffusion model for both ego and dynamic actors, conditioned on the generated 3D scene, and by propagating the grounded latents through time. Our framework produces physically consistent and temporally coherent 4D scenes, supporting controllable and realistic generation.
论文发现出处
这些页面提供论文线索，不代表同行评审或质量认证。
观测到的版本
平台记录号代表本站观测到的元数据变化，不是作者修订号。来源日期仅到月份或年份时，按该月或该年首日存档。
记录 #1 · 2026 · 最新观测版本
Grounded Latents for Entity-Centric 4D Scene Generation
Although recent work has explored generative modeling of 3D or 4D driving scenes, most approaches operate on dense voxel-based representations, which are computationally expensive and struggle to maintain temporal or structural consistency. These methods often produce blurred or merged entities (i.e., cars, trucks, pedestrians) and lack fine-grained control over individual scene elements. We propose LatentWorld, a framework for generative modeling in a compact, entity-centric latent space, where each grounded 3D latent represents a semantically meaningful local region of the scene. This formulation enables precise, consistent control of both foreground and background elements while preserving geometric detail. We further extend this representation to 4D by learning a motion diffusion model for both ego and dynamic actors, conditioned on the generated 3D scene, and by propagating the grounded latents through time. Our framework produces physically consistent and temporally coherent 4D scenes, supporting controllable and realistic generation.
查看此版本解读 →查看此版本原文 ↗