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Xuanmin Wang

dblp:402/7390 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Visual content generation and editing › 3d content generation
3d city generation
1.722025
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
1.122025
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
0.912025
MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025
Visual content generation and editing
3d content generation
0.912025
MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025
Visual content generation and editing
texture synthesis
0.312025
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
YearPublicationVenuePosition
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
ICCV2
2025 CitySculpt: 3D City Generation from Satellite Imagery with UV Diffusion
abstract
Generating 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 Multimedia2