CVDec 8, 2023

ControlRoom3D: Room Generation using Semantic Proxy Rooms

arXiv:2312.05208v172 citationsh-index: 33CVPR
Originality Incremental advance
AI Analysis

It addresses the challenge of manual 3D environment creation for AR/VR users, offering an incremental improvement over prior methods by enhancing layout plausibility.

The paper tackles the problem of generating plausible 3D room meshes for AR/VR by using semantic proxy rooms and 2D models, resulting in diverse and globally plausible outputs as validated by quantitative metrics and user evaluations.

Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by generating room meshes conditioned on textual style descriptions. Yet, many of these automatically generated 3D meshes do not adhere to typical room layouts, compromising their plausibility, e.g., by placing several beds in one bedroom. To address these challenges, we present ControlRoom3D, a novel method to generate high-quality room meshes. Central to our approach is a user-defined 3D semantic proxy room that outlines a rough room layout based on semantic bounding boxes and a textual description of the overall room style. Our key insight is that when rendered to 2D, this 3D representation provides valuable geometric and semantic information to control powerful 2D models to generate 3D consistent textures and geometry that aligns well with the proxy room. Backed up by an extensive study including quantitative metrics and qualitative user evaluations, our method generates diverse and globally plausible 3D room meshes, thus empowering users to design 3D rooms effortlessly without specialized knowledge.

Foundations

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