Abstract
Virtual furniture synthesis, which seamlessly integrates reference objects into indoor scenes while maintaining geometric coherence and visual realism, holds substantial promise for home design and e-commerce applications. However, this field remains underexplored due to the scarcity of reproducible benchmarks and the limitations of existing image composition methods in achieving high-fidelity furniture synthesis while preserving background integrity. To overcome these challenges, we first present RoomBench++, a comprehensive and publicly available benchmark dataset tailored for this task. It consists of 112,851 training pairs and 1,832 testing pairs drawn from both real-world indoor videos and realistic home design renderings, thereby supporting robust training and evaluation under practical conditions. Then, we propose RoomEditor++, a versatile diffusion-based architecture featuring a parameter-sharing dual diffusion backbone, which is compatible with both U-Net and DiT architectures. This design unifies the feature extraction and inpainting processes for reference and background images. Our in-depth analysis reveals that the parameter-sharing mechanism enforces aligned feature representations, facilitating precise geometric transformations, texture preservation, and seamless integration. Extensive experiments validate that RoomEditor++ is superior over state-of-the-art approaches in terms of quantitative metrics, qualitative assessments, and human preference studies, while highlighting its strong generalization to unseen indoor scenes and general scenes without task-specific fine-tuning. The dataset and source code are available at \url{this https URL}.
Abstract (translated)
虚拟家具合成技术将参考对象无缝集成到室内场景中，同时保持几何一致性和视觉逼真度，在家庭设计和电子商务应用方面展现出巨大的潜力。然而，由于缺乏可重复使用的基准数据集以及现有图像组合方法在实现高保真家具合成的同时难以保留背景完整性的限制，这一领域仍处于探索阶段。 为了克服这些挑战，我们首先提出了RoomBench++，这是一个全面且公开可用的基准数据集，专门针对此类任务设计。该数据集包括112,851对训练样本和1,832对测试样本，分别从真实世界的室内视频和逼真的家居设计渲染中提取，从而支持在实际条件下稳健地进行训练和评估。 接着，我们提出了RoomEditor++，这是一种基于扩散的多功能架构，具有参数共享双扩散主干结构，并且可以兼容U-Net和DiT架构。该设计统一了参考图像与背景图像的特征提取和修复过程。深入分析表明，参数共享机制强制执行一致的特征表示，从而促进了精确的几何变换、纹理保持以及无缝集成。 广泛的实验验证显示，RoomEditor++在定量指标、定性评估和人类偏好研究方面均优于当前最先进的方法，并且表现出强大的泛化能力，在未见过的室内场景及一般场景中无需特定任务微调就能发挥作用。数据集和源代码可在此网址获取：\url{this https URL}。
URL
https://arxiv.org/abs/2512.17573
