ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured
  Proxies
Authors: name: Jinyan Yuan, name: Bangbang Yang, name: Keke Wang, name: Panwang Pan, name: Lin Ma, name: Xuehai Zhang, name: Xiao Liu, name: Zhaopeng Cui, name: Yuewen Ma

Automatic creation of 3D scenes for immersive VR presence has been a
significant research focus for decades. However, existing methods often rely on
either high-poly mesh modeling with post-hoc simplification or massive 3D
Gaussians, resulting in a complex pipeline or limited visual realism. In this
paper, we demonstrate that such exhaustive modeling is unnecessary for
achieving compelling immersive experience. We introduce ImmerseGen, a novel
agent-guided framework for compact and photorealistic world modeling.
ImmerseGen represents scenes as hierarchical compositions of lightweight
geometric proxies, i.e., simplified terrain and billboard meshes, and generates
photorealistic appearance by synthesizing RGBA textures onto these proxies.
Specifically, we propose terrain-conditioned texturing for user-centric base
world synthesis, and RGBA asset texturing for midground and foreground scenery.
This reformulation offers several advantages: (i) it simplifies modeling by
enabling agents to guide generative models in producing coherent textures that
integrate seamlessly with the scene; (ii) it bypasses complex geometry creation
and decimation by directly synthesizing photorealistic textures on proxies,
preserving visual quality without degradation; (iii) it enables compact
representations suitable for real-time rendering on mobile VR headsets. To
automate scene creation from text prompts, we introduce VLM-based modeling
agents enhanced with semantic grid-based analysis for improved spatial
reasoning and accurate asset placement. ImmerseGen further enriches scenes with
dynamic effects and ambient audio to support multisensory immersion.
Experiments on scene generation and live VR showcases demonstrate that
ImmerseGen achieves superior photorealism, spatial coherence and rendering
efficiency compared to prior methods. Project webpage:
https://immersegen.github.io.