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Finetuning Prompt-Based Image Editor for Geometry-Consistent Room Defurnishing

2026-10-25 21:54:21
Prakhar Kulshreshtha, Brian Pugh, Zachary Nussdorfer, Salma Jiddi
arXiv_CV
       
Abstract

Erasing all furniture from a single room image to generate its empty counterpart is valuable for XR applications such as diminished reality and virtual staging. The challenge is twofold: inpainting the furniture regions with realistic content, and doing so without distorting the room's 3D geometry. Prompt-driven image editors, such as FLUX.2, handle realism impressively, but their structural faithfulness is inferior to explicit layout conditioned inpainting. To improve geometric consistency, we propose a training recipe for finetuning a LoRA on 2,500 furnished-empty room pairs. FLUX.2 with our LoRA adaptor significantly improves geometric consistency while retaining the high texture fidelity. This single-photo inpainter can be an effective building block for depth-aware and 3D-consistent XR pipelines.

Abstract (translated)

URL

https://arxiv.org/abs/2610.34597

PDF

https://arxiv.org/pdf/2610.34597.pdf

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