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Chengliang Ping

dblp:426/8727 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2025
0009-0003-7245-3826ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 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
Visual content generation and editing · 100%
Artificial intelligence
1 paper
3D vision · 77% Generative modeling · 23%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
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
3d city generation
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
Machine learning › Generative modeling
diffusion model
0.312025
MagicCity: Geometry-Aware 3D City Generation from Satellite Imagery with Multi-View Consistency · ICCV 2025

Methods — techniques the papers use, named apart from their topics

satellite imagery · 1.7multi-view consistency · 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
ICCV4
2025 Split-Screen Folklore: Human-AI Co-Creation in Reimagining The Legend of the White Snake
abstract
This paper introduces a novel framework for human–AI collaborative storytelling using split-screen narrative techniques. Moving beyond conventional AI applications in visual reproduction, we develop a co-creative workflow that integrates computational capabilities with Chinese aesthetic principles to address challenges in temporal and spatial narrative design. Through a case study of The Legend of the White Snake, we demonstrate how AI enhances the symbolic representation of duality, while human expertise ensures cultural authenticity. Key findings include: (1) AI improves efficiency in generating parallel visual sequences and maintaining stylistic consistency; (2) human intervention is essential for contextualizing cultural symbols and emotional resonance; (3) the hybrid approach enables innovative visual storytelling that both respects tradition and expands creative possibilities. This research contributes to culturally-grounded AI applications in digital heritage and narrative innovation.
Qi Xiang, Chengliang Ping, Luwen Yu, David Kei-Man Yip
VINCI3