Demo Data from EDBT 2026 / reviewers in the wild / expert
Jiawang Yu
dblp:338/4835
· DBLP profile ↗
3ranked-venue papers
1as first author
3since 2021 · last 2024
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 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.
| Computer graphics and multimedia
1 paper |
Computer animation and physical simulation · 50% Geometric modeling and processing · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer animation and physical simulation
cloth animation |
0.8 | 1 | 2024 | Super-Resolution Cloth Animation with Spatial and Temporal Coherence · ACM Trans. Graph. 2024 |
Geometric modeling and processing
mesh processing |
0.8 | 1 | 2024 | Super-Resolution Cloth Animation with Spatial and Temporal Coherence · ACM Trans. Graph. 2024 |
Methods — techniques the papers use, named apart from their topics
patch decomposition · 0.8mesh-based super-resolution · 0.8cloth simulation · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Similar norm more transferable: Rethinking feature norms discrepancy in adversarial domain adaptation
Jun Dan, Mushui Liu, Chunfeng Xie, Jiawang Yu, Haoran Xie 0004, Ruokun Li, Shunjie Dong |
Knowl. Based Syst. | 4 |
| 2024 | Super-Resolution Cloth Animation with Spatial and Temporal CoherenceabstractCreating super-resolution cloth animations, which refine coarse cloth meshes with fine wrinkle details, faces challenges in preserving spatial consistency and temporal coherence across frames. In this paper, we introduce a general framework to address these issues, leveraging two core modules. The first module interleaves a simulator and a corrector. The simulator handles cloth dynamics, while the corrector rectifies differences in low-frequency features across various resolutions. This interleaving ensures prompt correction of spatial errors from the coarse simulation, effectively preventing their temporal propagation. The second module performs mesh-based super-resolution for detailed wrinkle enhancements. We decompose garment meshes into overlapping patches for adaptability to various styles and geometric continuity. Our method achieves an 8× improvement in resolution for cloth animations. We showcase the effectiveness of our method through diverse animation examples, including simple cloth pieces and intricate garments. Jiawang Yu, Zhendong Wang 0001 |
ACM Trans. Graph. | 1 |
| 2023 | HOMDA: High-Order Moment-Based Domain Alignment for unsupervised domain adaptation
Jun Dan, Hao Chi, Jiawang Yu, Jinhai Zhou |
Knowl. Based Syst. | 5 |