Abstract
Generating high-quality, textured 3D scenes from a single image remains a fundamental challenge in vision and graphics. Recent image-to-3D generators recover reasonable geometry from single views, but their object-centric training limits generalization to complex, large-scale scenes with faithful structure and texture. We present EvoScene, a self-evolving, training-free framework that progressively reconstructs complete 3D scenes from single images. The key idea is combining the complementary strengths of existing models: geometric reasoning from 3D generation models and visual knowledge from video generation models. Through three iterative stages--Spatial Prior Initialization, Visual-guided 3D Scene Mesh Generation, and Spatial-guided Novel View Generation--EvoScene alternates between 2D and 3D domains, gradually improving both structure and appearance. Experiments on diverse scenes demonstrate that EvoScene achieves superior geometric stability, view-consistent textures, and unseen-region completion compared to strong baselines, producing ready-to-use 3D meshes for practical applications.
Abstract (translated)
从单张图像生成高质量、具有纹理的三维场景仍然是计算机视觉和图形学中的一个基本挑战。最近的一些图像到三维模型的生成器能够从单一视角恢复合理的几何结构，但它们以对象为中心的训练方式限制了其在复杂大规模场景中实现忠实结构和纹理的一般化能力。我们提出了EvoScene，这是一个自我演化的、无需训练的框架，它可以逐步地从单张图片中重建完整的三维场景。核心思想是结合现有模型的优势：即三维生成模型中的几何推理能力和视频生成模型中的视觉知识。 通过三个迭代阶段——空间先验初始化、基于视觉引导的三维场景网格生成和基于空间引导的新视角生成——EvoScene在二维和三维域之间交替进行，逐步提升结构和外观的质量。实验结果表明，在多样化的场景中，EvoScene相比于强大的基准模型实现了更优的几何稳定性、视图一致性的纹理以及未见区域的完成度，并且可以生成适合实际应用的即用型3D网格。
URL
https://arxiv.org/abs/2512.08905
