English

WonderZoom: Multi-Scale 3D World Generation

Computer Vision and Pattern Recognition 2025-12-11 v1 Artificial Intelligence Graphics

Abstract

We present WonderZoom, a novel approach to generating 3D scenes with contents across multiple spatial scales from a single image. Existing 3D world generation models remain limited to single-scale synthesis and cannot produce coherent scene contents at varying granularities. The fundamental challenge is the lack of a scale-aware 3D representation capable of generating and rendering content with largely different spatial sizes. WonderZoom addresses this through two key innovations: (1) scale-adaptive Gaussian surfels for generating and real-time rendering of multi-scale 3D scenes, and (2) a progressive detail synthesizer that iteratively generates finer-scale 3D contents. Our approach enables users to "zoom into" a 3D region and auto-regressively synthesize previously non-existent fine details from landscapes to microscopic features. Experiments demonstrate that WonderZoom significantly outperforms state-of-the-art video and 3D models in both quality and alignment, enabling multi-scale 3D world creation from a single image. We show video results and an interactive viewer of generated multi-scale 3D worlds in https://wonderzoom.github.io/

Keywords

Cite

@article{arxiv.2512.09164,
  title  = {WonderZoom: Multi-Scale 3D World Generation},
  author = {Jin Cao and Hong-Xing Yu and Jiajun Wu},
  journal= {arXiv preprint arXiv:2512.09164},
  year   = {2025}
}

Comments

Project website: https://wonderzoom.github.io/ The first two authors contributed equally

R2 v1 2026-07-01T08:18:04.520Z