Abstract
Unbounded 3D world generation is emerging as a foundational task for scene modeling in computer vision, graphics, and robotics. In this work, we present WorldFlow3D, a novel method capable of generating unbounded 3D worlds. Building upon a foundational property of flow matching - namely, defining a path of transport between two data distributions - we model 3D generation more generally as a problem of flowing through 3D data distributions, not limited to conditional denoising. We find that our latent-free flow approach generates causal and accurate 3D structure, and can use this as an intermediate distribution to guide the generation of more complex structure and high-quality texture - all while converging more rapidly than existing methods. We enable controllability over generated scenes with vectorized scene layout conditions for geometric structure control and visual texture control through scene attributes. We confirm the effectiveness of WorldFlow3D on both real outdoor driving scenes and synthetic indoor scenes, validating cross-domain generalizability and high-quality generation on real data distributions. We confirm favorable scene generation fidelity over approaches in all tested settings for unbounded scene generation. For more, see this https URL.
Abstract (translated)
无界3D世界生成正逐渐成为计算机视觉、图形学与机器人领域场景建模的基础任务。本研究提出WorldFlow3D，一种能够生成无界3D世界的新方法。基于流匹配的核心特性——即定义两个数据分布间的传输路径——我们将3D生成更一般地建模为流经3D数据分布的问题，而不局限于条件去噪。我们发现无隐式流方法能生成因果准确的三维结构，并以此作为中间分布来引导更复杂结构与高质量纹理的生成，同时收敛速度超越现有方法。我们通过矢量场景布局条件实现生成场景的可控性，并利用场景属性进行视觉纹理控制。该方法在真实户外驾驶场景与合成室内场景上均验证了有效性，证明了跨领域泛化能力及对真实数据分布的高质量生成。在所有测试的无界场景生成设置中，WorldFlow3D均展现出优于对比方法的场景生成保真度。更多详情请访问此链接。
URL
https://arxiv.org/abs/2603.29089
