Demo Data from VLDB 2026 / reviewers in the wild / expert
Beiqi Chen
dblp:365/3256
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-author · 2 since 2021Artificial intelligence and machine learning · 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.
| Computer graphics and multimedia
1 paper |
Rendering · 50% Geometric modeling and processing · 25% Visual content generation and editing · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering › gaussian splatting
4d gaussian splatting |
1.0 | 1 | 2026 | Style4D-Bench: A Benchmark Suite for 4D Stylization · AAAI 2026 |
Geometric modeling and processing › 3d scene representation
4d scene representation |
1.0 | 1 | 2026 | Style4D-Bench: A Benchmark Suite for 4D Stylization · AAAI 2026 |
Visual content generation and editing
style transfer |
1.0 | 1 | 2026 | Style4D-Bench: A Benchmark Suite for 4D Stylization · AAAI 2026 |
Rendering › non-photorealistic rendering
stylized rendering |
1.0 | 1 | 2026 | Style4D-Bench: A Benchmark Suite for 4D Stylization · AAAI 2026 |
Methods — techniques the papers use, named apart from their topics
gaussian splatting · 1.0contrastive coherence learning · 1.0MLP · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Style4D-Bench: A Benchmark Suite for 4D StylizationabstractWe introduce Style4D-Bench, the first benchmark suite specifically designed for 4D stylization, with the goal of standardizing evaluation and facilitating progress in this emerging area. Style4D-Bench comprises: 1) a strong baseline that make an initial attempt for 4D stylization, 2) a comprehensive evaluation protocol measuring spatial fidelity, temporal coherence, and multi-view consistency through both perceptual and quantitative metrics, and 3) a curated collection of high-resolution dynamic 4D scenes with diverse motions and complex backgrounds. To establish a strong baseline, we present Style4D, a novel framework built upon 4D Gaussian Splatting. It consists of three key components: a basic 4DGS scene representation to capture reliable geometry, a Style Gaussian Representation that leverages lightweight per-Gaussian MLPs for temporally and spatially aware appearance control, and a Holistic Geometry-Preserved Style Transfer module designed to enhance spatio-temporal consistency via contrastive coherence learning and structural content preservation. Extensive experiments on Style4D-Bench demonstrate that Style4D achieves state-of-the-art performance in 4D stylization, producing fine-grained stylistic details with stable temporal dynamics and consistent multi-view rendering. We expect Style4D-Bench to become a valuable resource for benchmarking and advancing research in stylized rendering of dynamic 3D scenes. Beiqi Chen, Haitang Feng, Jian-Huang Lai, Jianlou Si, Guangcong Wang |
AAAI | 1 |
| 2026 | Consistency-preserving Gaussian splatting for block-based large-scale scene reconstruction
Beiqi Chen, Shengfang Pan, Niansheng Liu, Jinhe Su |
Comput. Graph. | 2 |