Qinghe Wang | 王清和

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I am a third-year PhD student at the IIAU-Lab in Dalian University of Technology, under the supervision of Prof. Huchuan Lu (IEEE Fellow) and Prof. Xu Jia. I am selected into the China Association for Science and Technology (CAST) Young Talent Support Program - Doctoral Student Initiative. I obtained my Master's degree from Tianjin University supervised by Prof. Pengfei Zhu and Prof. Bing Cao. I obtained my Bachelor's degree from Dalian Maritime University. My research focuses on Video World Models.

[GitHub] [Google Scholar] [X] [E-mail: aitwqh@163.com]

Graduating in 2027, actively seeking 2026 recruitment opportunities. Please feel free to contact me!

Internships

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Alibaba
2026.5~Now Research Intern in HappyOyster Team, Alibaba Group (Beijing)  
Advisor: Wenbo Su
Topic: Real-Time World Creation and Interaction (Directing Mode)

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Kuaishou
2024.7~2026.4 Research Intern in Kling Team, Kuaishou Technology (Shenzhen)  
Advisors: Xiaoyu Shi, Xintao Wang
Topic: Video Generation

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ByteDance
2022.4~2022.12 Research Intern in ByteDance Intelligent Creation Lab (Beijing)  
Advisors: Lijie Liu, Qian He
Topic: Image Generation

Selected Publications

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[CVPR 2026 CCF A]  
MultiShotMaster: A Controllable Multi-Shot Video Generation Framework
Qinghe Wang, Xiaoyu Shi✉, Baolu Li, Weikang Bian, Quande Liu, Huchuan Lu, Xintao Wang, Pengfei Wan, Kun Gai, Xu Jia✉
[Paper] [Project Page] [Code] [🥇 1st Place at AAAI CVM 2026]

The first controllable multi-shot video generation framework that supports text-driven inter-shot consistency, customized subject with motion control, and background-driven customized scene. Both shot counts and shot durations are variable.

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[SIGGRAPH 2026 CCF A]  
VFXMaster: Unlocking Dynamic Visual Effect Generation via In-Context Learning
Baolu Li*, Yiming Zhang*, Qinghe Wang(co-first, Project Lead), Liqian Ma✉, Xiaoyu Shi, Xintao Wang, Pengfei Wan, Zhenfei Yin, Yunzhi Zhuge, Huchuan Lu, Xu Jia✉
[Paper] [Project Page] [Code]

A unified reference-based VFX video generation framework allows users to reproduce diverse dynamic effects from reference videos onto target content.

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[CVPR 2026 CCF A]  
Group Editing: Edit Multiple Images in One Go
Yue Ma, Xinyu Wang, Qianli Ma, Qinghe Wang✉(co-corresponding), Mingzhe Zheng, Xiangpeng Yang, Hao Li, Chongbo Zhao, Jixuan Ying, Harry Yang, Hongyu Liu✉, Qifeng Chen
[Paper] [Project Page] [Code]

A unified group-image editing framework enables users to apply consistent and diverse editing across multiple related images by leveraging explicit geometric correspondences and implicit video priors.

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[CVPR 2026 Findings CCF A]  
Video2LoRA: Unified Semantic-Controlled Video Generation via Per-Reference-Video LoRA
Zexi Wu, Qinghe Wang✉(co-corresponding), Jing Dai, Baolu Li, Yiming Zhang, Yue Ma✉, Xu Jia, Hongming Xu✉
[Paper] [Code]

A unified framework takes a reference video containing the desired semantics as input and employs a HyperNetwork to generate lightweight, semantic-specific LoRA modules for semantic-controllable video generation.

 

[SIGGRAPH 2026 CCF A]  
EasyVFX: Frequency-Driven Decoupling for Resource-Efficient VFX Generation
Yue Ma, Xu Ye, Qinghe Wang✉(co-corresponding), Yucheng Wang, Hongyu Liu, Yinhan Zhang, Xinyu Wang, Yuanpeng Che, Shanhui Mo, Paul Liang, Fangneng Zhan✉, Qifeng Chen
[Paper] [Project Page] [Code]

A VFX video generation framework takes a reference VFX video as input and employs a frequency-aware decoupling mechanism to enable resource-efficient, high-fidelity visual effect transfer for controllable video generation.

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[SIGGRAPH 2025 CCF A]  
CineMaster: A 3D-Aware and Controllable Framework for Cinematic Text-to-Video Generation
Qinghe Wang*, Yawen Luo*(co-first), Xiaoyu Shi✉, Xu Jia✉, Huchuan Lu, Tianfan Xue✉, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai.
[Paper] [Project Page]

A 3D-aware and controllable text-to-video generation method allows users to manipulate objects and camera jointly in 3D space for high-quality cinematic video creation.

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[TIP 2025 CCF A; CAS-JCR Q1 Top]  
CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
Qinghe Wang, Baolu Li, Xiaomin Li, Bing Cao, Liqian Ma, Huchuan Lu, Xu Jia✉.
[Gradio Demo] [Paper] [Project Page] [Code]

An end-to-end framework allows users to sample new characters with consistent identities for new character creation.

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[TMM 2025 CCF A; CAS-JCR Q1 Top]  
StableIdentity: Inserting Anybody into Anywhere at First Sight
Qinghe Wang, Xu Jia✉, Xiaomin Li, Taiqing Li, Liqian Ma, Yunzhi Zhuge, Huchuan Lu.
[Paper] [Project Page] [Code] [Yann LeCun: OMG]

A framework enables identity-consistent generation with just one face image, and seamless integration with video/3D generation without finetuning.

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[Arxiv 2023]  
HS-Diffusion: Semantic-Mixing Diffusion for Head Swapping
Qinghe Wang, Lijie Liu, Miao Hua, Pengfei Zhu, Wangmeng Zuo, Qinghua Hu, Huchuan Lu, Bing Cao✉.
[Paper] [Code]

A framework enables seamless head swapping, preserving both head and body details while generating natural transition regions.

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[TNNLS 2023 CAS-JCR Q1 Top; CCF B]  
Multi-view knowledge ensemble with frequency consistency for cross-domain face translation
Bing Cao, Qinghe Wang, Pengfei Zhu✉, Qinghua Hu, Dongwei Ren, Wangmeng Zuo, Xinbo Gao.
[Paper] [Code]

A framework enables cross-domain face translation with structural and identity preservation.

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[中国科学：信息科学 2022 CCF A类中文期刊]  
基于自判别循环生成对抗网络的人脸图像翻译 (Nickname: CycleGAN--)
王清和, 曹兵✉, 朱鹏飞, 王楠楠, 胡清华, 高新波.
[Paper] [Code]

A framework enables cross-domain face translation by integrating two the discriminators into two encoders.

Service

I serve as a reviewer for TPAMI, TIP, SIGGRAPH, CVPR, ECCV, TNNLS, TCSVT, ICLR, ACM MM, ICME, MIR, etc.

Awards