WorldFlow3D: Flowing Through 3D Distributions for Unbounded World Generation

Unbounded 3D world generation is emerging as a founda-tional task for scene modeling in computer vision, graphics, and robotics.In this work, we present WorldFlow3D, a novel method capable of gen-erating unbounded 3D worlds. Building upon a foundational property offlow matching – namely, defining a path of transport between two datadistributions – we model 3D generation more generally as a problemof flowing through 3D data distributions, not limited to conditional de-noising. We find that our latent-free flow approach generates causal andaccurate 3D structure, and can use this as an intermediate distribution toguide the generation of more complex structure and high-quality texture– all while converging more rapidly than existing methods. We enablecontrollability over generated scenes with vectorized scene layout condi-tions for geometric structure control and visual texture control throughscene attributes. We confirm the effectiveness of WorldFlow3D on bothreal outdoor driving scenes and synthetic indoor scenes, validating cross-domain generalizability and high-quality generation on real data distri-butions. We confirm favorable scene generation fidelity over approachesin all tested settings for unbounded scene generation.