VLDB 2026 Research / reviewers in the wild / expert
Wooseok Song
dblp:352/5576
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 first-author · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 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 |
Generative modeling · 87% 3D vision · 13% | |
| Computer graphics and multimedia
1 paper |
Visual content generation and editing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Generative modeling
diffusion model |
0.9 | 1 | 2025 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025 |
Machine learning › Generative modeling › diffusion model
guided diffusion |
0.9 | 1 | 2025 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025 |
Visual content generation and editing
3d content generation |
0.9 | 1 | 2025 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025 |
Computer vision › 3D vision
3d scene understanding |
0.3 | 1 | 2025 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025 |
Methods — techniques the papers use, named apart from their topics
diffusion guidance · 1.7LLM-based layout control · 1.73d gaussian splatting · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion GuidanceabstractWhile single-concept customization has been studied in 3D, multi-concept customization remains largely unexplored. To address this, we propose MultiDreamer3D that can generate coherent multi-concept 3D content in a divide-and-conquer manner. First, we generate 3D bounding boxes using an LLM-based layout controller. Next, a selective point cloud generator creates coarse point clouds for each concept. These point clouds are placed in the 3D bounding boxes and initialized into 3D Gaussian Splatting with concept labels, enabling precise identification of concept attributions in 2D projections. Finally, we refine 3D Gaussians via concept-aware interval score matching, guided by concept-aware diffusion. Our experimental results show that MultiDreamer3D not only ensures object presence and preserves the distinct identities of each concept but also successfully handles complex cases such as property change or interaction. To the best of our knowledge, we are the first to address the multi-concept customization in 3D. Wooseok Song, Seunggyu Chang, Jaejun Yoo 0001 |
IJCAI | 1 |