[Submitted on 30 Oct 2025 (v1), last revised 18 Jul 2026 (this version, v2)]

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Abstract:There are two prevalent ways for automatic 3D scene construction: procedural generation and 2D lifting. Among these, panorama-based 2D lifting has emerged as a promising technique, leveraging powerful 2D generative priors to produce immersive, realistic, and diverse 3D environments. In this work, we advance this technique to generate graphics-ready 3D scenes suitable for physically based rendering (PBR), relighting, and simulation. Our key insight is to repurpose 2D generative models for panorama perception of geometry, textures, and PBR materials. Unlike existing 2D lifting approaches that emphasize appearance generation and neglect the perception of intrinsic properties, we present OmniX, a versatile and unified framework for panorama generation, perception, and completion. Built upon cross-modal adapter structure and cyclic spatial operators, OmniX effectively repurposes pre-trained 2D flow matching priors for joint modeling of multimodal, seamless equirectangular representations. Furthermore, we construct a large-scale synthetic panorama dataset comprising high-quality multimodal panoramas from diverse indoor and outdoor scenes. Extensive experiments demonstrate the effectiveness and generality of OmniX as a unified framework for panorama generation and perception across geometry, lighting, and semantics, enabling graphics-ready 3D scene generation and opening new possibilities for immersive and physically realistic virtual world creation.

Submission history

From: Yukun Huang [view email]
[v1] Thu, 30 Oct 2025 17:59:51 UTC (8,210 KB)
[v2] Sat, 18 Jul 2026 16:08:00 UTC (9,724 KB)