NAVE	Networked Augmented Virtual Environment (NAVE) Group

Publication:Yusen Liu, Xinyu Zhang, Qichuan Geng, and Zhong Zhou. DGDiff: Immersive 3D Indoor Scene Synthesis via Dialog-Graph Conditioned Diffusion [C]. Proceedings of  the 24th IEEE International Symposium on Mixed and Augmented Reality (ISMAR'2025), Daejeon, South Korea, Oct. 8 - Oct. 12, 2025: 444-453. (CCF rank-B conference) pdf         Immersive 3D indoor scene synthesis is essential for applications such as AR/VR and 3D content creation. However, existing approaches fail to meet the immersive AR/VR requirements for fidelity, user-system interaction, and production speed simultaneously. Traditional scene synthesis methods are overly rigid, limiting user interactivity, whereas large language model (LLM)-based approaches suffer from slow response times and imprecise spatial structuring. To address these issues, we propose DGDiff, a novel dialog-graph conditioned diffusion framework for immersive, controllable, continuous synthesis and editing of 3D indoor scenes. This framework combines a conversational module powered by LLMs with a multimodal diffusion model. The conversational module translates user dialogue into structured semantic graphs, while the diffusion model integrates textual and graph-based conditions to synthesize realistic, editable indoor scenes. Experimental results demonstrate that DGDiff outperforms single-modality baselines, achieving an improvement of over 10 % in FID and a reduction of approximately 30 % in response time for dynamic scene interactive editing, offering an immersive and user-friendly synthesis experience. Project page: https://gitee.com/VR_NAVE/dgdiff.git create by admin at 2026-03-27 13:13:45