Demo Data from EDBT 2026 / reviewers in the wild / expert
Xiangzhong Zhou
dblp:440/6775
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0004-5052-5160ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, 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.
| Computer graphics and multimedia
1 paper |
Rendering · 100% | |
| Artificial intelligence
1 paper |
Generative modeling · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering › lighting
lighting design |
1.0 | 1 | 2026 | LightingGen: A DMX Based Generation Method for Entertainment Stage Lighting · IEEE Trans. Multim. 2026 |
Machine learning › Generative modeling › diffusion model
conditional generation |
0.3 | 1 | 2026 | LightingGen: A DMX Based Generation Method for Entertainment Stage Lighting · IEEE Trans. Multim. 2026 |
Machine learning › Generative modeling › music generation
conditional music generation |
0.3 | 1 | 2026 | LightingGen: A DMX Based Generation Method for Entertainment Stage Lighting · IEEE Trans. Multim. 2026 |
Methods — techniques the papers use, named apart from their topics
latent representation · 2.0generative model · 2.0conditional sampling · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | LightingGen: A DMX Based Generation Method for Entertainment Stage LightingabstractFor the scenes of stage lighting design in entertainment concerts, this paper proposes a DMX-based generative model, LightingGen, to simulate the lighting design and programming process of human experts. Unlike previous emotion-based lighting design approaches, LightingGen employs a data-driven method from the creator's perspective, standardizing stage lighting control parameters through DMX signals to create a unified lighting feature representation called DULA. LightingGen incorporates a two-stage generative model: the first stage uses LightingCodec to generate latent representations of lighting features, and the second stage uses music as a condition to perform conditional sampling in the latent spaces of lighting. To validate our methods, A virtual stage environment was built to collect 2.62 hours of DMX lighting control sequences as dataset. Experimental results indicate that LightingGen can generate lighting control parameters that partially align with pop music. To our knowledge, LightingGen is the first work to use generative methods to model the relationship between pop music and stage lighting. Yujian Jiang, Xiangzhong Zhou, Xuzeng Guan |
IEEE Trans. Multim. | 4 |