{"ID":2827099,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.17573","arxiv_id":"2512.17573","title":"RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis","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{https://github.com/stonecutter-21/roomeditor}.","short_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...","url_abs":"https://arxiv.org/abs/2512.17573","url_pdf":"https://arxiv.org/pdf/2512.17573v1","authors":"[\"Qilong Wang\",\"Xiaofan Ming\",\"Zhenyi Lin\",\"Jinwen Li\",\"Dongwei Ren\",\"Wangmeng Zuo\",\"Qinghua Hu\"]","published":"2025-12-19T13:39:43Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false,"code_links":[{"ID":605785,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827099,"paper_url":"https://arxiv.org/abs/2512.17573","paper_title":"RoomEditor++: A Parameter-Sharing Diffusion Architecture for High-Fidelity Furniture Synthesis","repo_url":"https://github.com/stonecutter-21/roomeditor","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}