Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor Scene Generation
Authors: Zhifei Yang, Keyang Lu, Chao Zhang, Jiaxing Qi, Hanqi Jiang, Ruifei Ma, Shenglin Yin, Yifan Xu, Mingzhe Xing, Zhen Xiao, Jieyi Long, Xiangde Liu, Guangyao Zhai
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate that MMGDreamer exhibits superior control of object geometry, achieving state-of-the-art scene generation performance. Extensive experiments on the SG-FRONT dataset demonstrate that MMGDreamer attains higher fidelity and geometric controllability, and achieves state-of-the-art performance in scene synthesis, outperforming existing methods by a large margin. |
| Researcher Affiliation | Collaboration | 1 School of Computer Science, Peking University 2 School of Artificial Intelligence, Beihang University 3 Beijing Digital Native Digital City Research Center 4 School of Computer Science and Engineering, Beihang University 5 Theta Labs, Inc. 6 Technical University of Munich |
| Pseudocode | No | The paper describes methods using mathematical formulas and architectural descriptions of modules but does not contain explicit pseudocode or algorithm blocks. |
| Open Source Code | No | Project page https://yangzhifeio.github.io/project/MMGDreamer. The paper provides a project page link, which is typically a demonstration or overview page, but does not explicitly state that the source code for the methodology is openly available or provide a direct link to a code repository. |
| Open Datasets | Yes | We validate our approach using the SG-FRONT dataset (Zhai et al. 2024c), which provides comprehensive scene-graph annotations for indoor scenes. |
| Dataset Splits | No | The paper mentions using the SG-FRONT dataset and extracted images from 3D-FUTURE, but it does not specify any training/test/validation split percentages or counts. It states: "We then applied a random mask to mask the text, images, and relationships between nodes in the Full-Modality Graph, producing the Mixed-Modality Graph." |
| Hardware Specification | Yes | All experiments are performed on a single NVIDIA A100 GPU with 80 GB memory. |
| Software Dependencies | No | The paper mentions using the Adam W optimizer but does not provide specific version numbers for any programming languages, libraries, or other software dependencies. |
| Experiment Setup | Yes | We train our models using the Adam W optimizer, initializing the learning rate at 1 10 4 and utilizing a batch size of 128. The weighting factors for our loss components, α1 and α2, are consistently set to 1.0. |