Demo Data from SIGIR 2026 / reviewers in the wild / expert
Xuanmin Wang
dblp:402/7390
· DBLP profile ↗
2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0004-1023-6727ORCID · corroborated
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 since 2021Artificial intelligence and machine learning · 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
2 papers |
Visual content generation and editing · 100% | |
| Artificial intelligence
2 papers |
Generative modeling · 56% 3D vision · 44% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Visual content generation and editing › 3d content generation
3d city generation |
1.7 | 2 | 2025 | CitySculpt: 3D City Generation from Satellite Imagery with UV Diffusion · ACM Multimedia 2025 MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025 |
Machine learning › Generative modeling
diffusion model |
1.1 | 2 | 2025 | CitySculpt: 3D City Generation from Satellite Imagery with UV Diffusion · ACM Multimedia 2025 MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025 |
Computer vision › 3D vision
3d scene reconstruction |
0.9 | 1 | 2025 | MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025 |
Visual content generation and editing
3d content generation |
0.9 | 1 | 2025 | MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025 |
Visual content generation and editing
texture synthesis |
0.3 | 1 | 2025 | CitySculpt: 3D City Generation from Satellite Imagery with UV Diffusion · ACM Multimedia 2025 |
Methods — techniques the papers use, named apart from their topics
satellite imagery · 1.7normal diffusion · 1.7multi-view consistency · 1.7cross-attention · 1.7UV diffusion · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency
Xingbo Yao, Xuanmin Wang, Hao Wu 0094, Chengliang Ping, Doudou Zhang, Hui Xiong 0001 |
ICCV | 2 |
| 2025 | CitySculpt: 3D City Generation from Satellite Imagery with UV DiffusionabstractGenerating 3D cities from satellite imagery opens up new avenues for gaming, urban planning, and cinematic production. However, the limited information from satellite views presents significant challenges, hindering existing methods from generating high-quality cities that meet application standards. To address these challenges, we propose CitySculpt, a UV diffusion-based framework for generating 3D cities with high-fidelity geometry and photorealistic textures. Specifically, we first generate the detailed 3D geometries by refining coarse structures using a UV normal diffusion network. Building on these refined geometries, we introduce a texture generation approach that produces photorealistic textures despite the limited satellite information. To ensure style consistency across multiple objects, we design a cross-attention mechanism that enables feature sharing among them. Additionally, we contribute the CitySculpt dataset, a collection of high-quality 3D urban assets with multi-view renderings and comprehensive annotations to advance research in 3D city generation. Experiments demonstrate that CitySculpt outperforms state-of-the-art approaches in both generating detailed individual buildings and creating cities with high visual quality and rich architectural details. Xingbo Yao, Xuanmin Wang, Hui Xiong 0001 |
ACM Multimedia | 2 |