OmniX: From Unified Panoramic Generation and Perception To Graphics-Ready 3D Scenes

There are two prevalent ways for automatic 3D scene con-struction: procedural generation and 2D lifting. Among these, panorama-based 2D lifting has emerged as a promising technique, leveraging pow-erful 2D generative priors to produce immersive, realistic, and diverse3D environments. In this work, we advance this technique to generategraphics-ready 3D scenes suitable for physically based rendering (PBR),relighting, and simulation. Our key insight is to repurpose 2D generativemodels for panorama perception of geometry, textures, and PBR mate-rials. Unlike existing 2D lifting approaches that emphasize appearancegeneration and neglect the perception of intrinsic properties, we presentOmniX, a versatile and unified framework for panorama generation, per-ception, and completion. Built upon cross-modal adapter structure andcyclic spatial operators, OmniX effectively repurposes pre-trained 2Dflow matching priors for joint modeling of multimodal, seamless equirect-angular representations. Furthermore, we construct a large-scale syn-thetic panorama dataset comprising high-quality multimodal panoramasfrom diverse indoor and outdoor scenes. Extensive experiments demon-strate the effectiveness and generality of OmniX as a unified frameworkfor panorama generation and perception across geometry, lighting, andsemantics, enabling graphics-ready 3D scene generation and opening newpossibilities for immersive and physically realistic virtual world creation.Project page is available at https://yukun-huang.github.io/OmniX/.