Demo Data from EDBT 2026 / reviewers in the wild / expert
Wangyuanfan Bai
dblp:409/7286
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
3D vision · 46% Generative modeling · 23% Graph learning · 23% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › 3d generation
3d scene generation |
0.9 | 1 | 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts · CVPR 2025 |
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts · CVPR 2025 |
Machine learning › Graph learning
graph diffusion |
0.9 | 1 | 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts · CVPR 2025 |
Computer vision › 3D vision › 3d scene understanding › scene synthesis
indoor scene synthesis |
0.9 | 1 | 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts · CVPR 2025 |
Computer vision › Vision and language
vision-language model |
0.3 | 1 | 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free Prompts · CVPR 2025 |
Methods — techniques the papers use, named apart from their topics
vision-language model · 0.9diffusion transformer · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | FreeScene: Mixed Graph Diffusion for 3D Scene Synthesis from Free PromptsabstractControllability plays a crucial role in the practical applications of 3D indoor scene synthesis. Existing works either allow rough language-based control, that is convenient but lacks fine-grained scene customization, or employ graph-based control, which offers better controllability but demands considerable knowledge for the cumbersome graph design process. To address these challenges, we present FreeScene, a user-friendly framework that enables both convenient and effective control for indoor scene synthesis. Specifically, FreeScene supports free-form user inputs including text description and/or reference images, allowing users to express versatile design intentions. The user inputs are adequately analyzed and integrated into a graph representation by a VLM-based Graph Designer. We then propose MG-DiT, a Mixed Graph Diffusion Transformer, which performs graph-aware denoising to enhance scene generation. Our MG-DiT not only excels at preserving graph structure but also offers broad applicability to various tasks, including, but not limited to, text-to-scene, graph-to-scene, and rearrangement, all within a single model. Extensive experiments demonstrate that FreeScene provides an efficient and user-friendly solution that unifies text-based and graph-based scene synthesis, outperforming state-of-the-art methods in terms of both generation quality and controllability in a range of applications. Tongyuan Bai, Wangyuanfan Bai, Dong Chen 0044, Tieru Wu, Manyi Li, Rui Ma 0011 |
CVPR | 2 |