DreamHome-Pano: Design-Aware and Conflict-Free Panoramic Interior Generation
Lulu Chen, Yijiang Hu, Yuanqing Liu, Yulong Li, Yue Yang

TL;DR
DreamHome-Pano is a novel framework for generating high-quality, structurally consistent panoramic interior images that harmonize stylistic preferences with architectural constraints using advanced control and translation techniques.
Contribution
It introduces a semantic translation bridge and a conflict-free control architecture to improve the harmony between style and structure in panoramic interior generation.
Findings
Achieves better aesthetic and structural balance in generated interiors.
Outperforms existing methods in maintaining layout integrity.
Provides a new benchmark and training pipeline for panoramic interior synthesis.
Abstract
In modern interior design, the generation of personalized spaces frequently necessitates a delicate balance between rigid architectural structural constraints and specific stylistic preferences. However, existing multi-condition generative frameworks often struggle to harmonize these inputs, leading to "condition conflicts" where stylistic attributes inadvertently compromise the geometric precision of the layout. To address this challenge, we present DreamHome-Pano, a controllable panoramic generation framework designed for high-fidelity interior synthesis. Our approach introduces a Prompt-LLM that serves as a semantic bridge, effectively translating layout constraints and style references into professional descriptive prompts to achieve precise cross-modal alignment. To safeguard architectural integrity during the generative process, we develop a Conflict-Free Control architecture that…
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Taxonomy
Topics3D Shape Modeling and Analysis · Generative Adversarial Networks and Image Synthesis · Architecture and Computational Design
