{"ID":5676797,"CreatedAt":"2026-07-03T03:29:23.032456456Z","UpdatedAt":"2026-07-07T01:06:03.009715918Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.02407","arxiv_id":"2607.02407","title":"Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments","abstract":"Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to high geometric violations and low physical fidelity. To address this challenge, we propose SPG-Layout, a novel text-driven framework designed to generate physically plausible indoor scenes within complex non-Manhattan environments. Specifically, we first utilize statistical priors of object distributions to guide the training process, enhancing environmental understanding and fidelity. Furthermore, mirroring human design workflows, we adopt a hierarchical layout strategy that prioritizes the placement of large objects, thereby substantially minimizing layout violations. By synergizing these components, SPG-Layout achieves a balanced optimization of semantic realism and physical plausibility. To evaluate performance in these complex settings, we constructed a new benchmark comprising 500 diverse non-Manhattan environments. Extensive experiments demonstrate that SPG-Layout consistently and significantly outperforms existing methods across both Manhattan and non-Manhattan environments. The code will be publicly released.","short_abstract":"Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible object layout patterns in non-Manhattan settings, primarily because they struggle to model non-orthogonal spatial relationships, leading to...","url_abs":"https://arxiv.org/abs/2607.02407","url_pdf":"https://arxiv.org/pdf/2607.02407v1","authors":"[\"Xianhui Meng\",\"Zirui Song\",\"Yuchen Zhang\",\"Li Zhang\",\"Yongxuan Lv\",\"Xiuying Chen\",\"Kun Wang\",\"Yan Luo\",\"Kai Chen\",\"Hangjun Ye\",\"Long Chen\",\"Jun Liu\",\"Xiaoshuai Hao\"]","published":"2026-07-02T16:40:08Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.CV\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}