Wensheng Li(李文盛)

I am currently working toward the Ph.D. degree with School of Computer Science and Engineering, Sun Yat-sen University, supervised by Prof. Chengying Gao and Prof. Ning Liu in Intelligent and Multimedia Science Laboratory. I received the B.S. and M.S. degree in computer science and technology from Sun Yat-sen University, Guangzhou, China, in 2018 and 2021, respectively.

I'm interested in computer vision and computer graphics, with a particular focus on human pose estimation, human body reconstruction, and neural rendering.

Email  /  Github

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Education

  • Sept. 2022 - Now: Ph.D. in Sun Yat-sen University (Major: Computer Science)
  • Sept. 2019 - Aug. 2021: M.Sc. in Sun Yat-sen University (Major: Software Engineering)
  • Sept. 2014 - June 2018: B.Eng. in Sun Yat-sen University (Major: Software Engineering)

Publications

Feature Replacement in Gaussian Splatting for 3D Stylization
Jinkeng Zhu, Wensheng Li, Chengying Gao*,
Computer Graphics International (CGI, 2025)
[pdf]

We introduce a feature replacement module that utilizes reversible network to decouple content and style features, ensuring the effective substitution of style information while preserving scene content.

Efficient Integration of Neural Representations for Dynamic Humans
Wensheng Li, Lingzhe Zeng, Chengying Gao, Ning Liu*
IEEE Transactions on Visualization and Computer Graphics (TVCG, 2024)
[pdf]

We present a novel approach for efficiently modeling dynamic humans and achieving realistic renderings by integrating neural human representations.

DanceComposer: Dance-to-Music Generation Using a Progressive Conditional Music Generator
Xiao Liang, Wensheng Li, Lifeng Huang, Chengying Gao*
IEEE Transactions on Multimedia (TMM, 2024)
[pdf]

We propose DanceComposer, a framework for the automatic generation of appropriate music from dance videos.

3D interacting hand pose and shape estimation from a single RGB image
Chengying Gao*, Yujia Yang, Wensheng Li,
Neurocomputing, 2022
[pdf]

In this paper, we tackle the shape and pose estimation task of interacting hands from a single RGB image and outperform other methods by a large margin on the InterHand2.6M dataset.


Page inspired by Jon Barron.