Zisen Shao 邵子森

I am a first-year master's student in Computer Science at the University of Maryland, College Park, where I am advised by Prof. Ruohan Gao.

Previously, I earned my bachelor's degree in Computer Science from the University of Wisconsin–Madison, where I received advice from Prof. Josiah Hanna. I also participated in WisTex United at RoboCup, led by Prof. Josiah Hanna and Prof. Peter Stone.

I'm interested in computer vision and computer graphics. My research focuses on physics-informed models that can (1) reconstruct the digital twins of our Multimodal World from multimodal data, and (2) leverage shared representations across modalities to enable cross-modal learning.

Email  /  Github  /  LinkedIn

profile photo

Aurora over Lake Mendota

Publications
FreeFix: Boosting 3D Gaussian Splatting via Fine-Tuning-Free Diffusion Models
Hongyu Zhou, Zisen Shao, Sheng Miao, Pan Wang, Dongfeng Bai, Bingbing Liu, Yiyi Liao
3DV, 2026
project page / arXiv / code

A fine-tuning-free approach designed to eliminate artifacts and boost the rendering quality of 3D Gaussian Splatting (3DGS) in extrapolated views

WeRef: An Open-source and Extensible Dataset for Referee Gesture Recognition in RoboCup
Zisen Shao, Josiah P. Hanna,
RoboCup-2025: Robot Soccer World Cup XXVIII, 2025   (Oral Presentation)
code

WeRef is an open-source synthetic data generation pipeline for RoboCup Standard Platform League (SPL) referee gestures recognition.

Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer
Adam Labiosa, Zhihan Wang, Siddhant Agarwal, William Cong, Geethika Hemkumar, Abhinav Narayan Harish, Benjamin Hong, Josh Kelle, Chen Li, Yuhao Li, Zisen Shao, Peter Stone, Josiah P. Hanna
ICRA, 2025   (Champion of RoboCup SPL Challenge Shield Division 2024, Best RoboCup-Themed Paper Award @ Roboletics 2.0)
arXiv / code / media

A novel architecture integrating RL within a classical robotics stack, while employing a multi-fidelity sim2real approach and decomposing behavior into learned sub-behaviors with heuristic selection.


Thanks to this amazing template!