Wooseok Song

dblp:352/5576 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Machine learning › Generative modeling
diffusion model
0.912025
MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025
Machine learning › Generative modeling › diffusion model
guided diffusion
0.912025
MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025
Visual content generation and editing
3d content generation
0.912025
MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance · IJCAI 2025
Computer vision › 3D vision
3d scene understanding
0.312025
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
YearPublicationVenuePosition
2025 MultiDreamer3D: Multi-concept 3D Customization with Concept-Aware Diffusion Guidance
abstract
While 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
IJCAI1