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Ting-Hsuan Chen I am currently a master's student in Computer Science at the University of Southern California, advised by Professor Yue Wang. I have previously worked as an R&D engineer at Foxconn and also served as a research assistant in Professor Yu-Lun Liu's laboratory at National Yang Ming Chiao Tung University. Currently, I am working at Bosch as a Research Intern focusing on Scene Understanding and Generative AI. |
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ResearchI'm interested in computer vision, deep learning, generative AI, 3D reconstruction, autonomous agent, and robotics. |
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MoonSim: A Photorealistic Lunar Environment Simulator
Ting-Hsuan Chen*, Henghui Bao*, Ziyu Chen*, Haozhe Lou, Ge Yang, Zhiwen Fan, Marco Pavone, Yue Wang, CVPR, 2025 (under review) 
project page MoonSim is a photo-realistic lunar scene simulator that incorporates Unreal Engine for high-quality lunar images with realistic lighting and shadows and MuJoCo for physics simulation, supporting diverse locomotion and navigation tasks. |
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NaRCan: Natural Refined Canonical Image with Integration of Diffusion Prior for Video Editing
Ting-Hsuan Chen, Jiewen Chan, Hau-Shiang Shiu, Shih Han Yen, Changhan Yeh, Yu-Lun Liu, NeurIPS, 2024
project page / arXiv / code / demo NaRCan, a video editing framework, integrates a hybrid deformation field network with diffusion priors to address the challenge of maintaining the canonical image as a natural image. |
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DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models
Changhan Yeh, Chin-Yang Lin, Zhixiang Wang, Chi-Wei Hsiao, Ting-Hsuan Chen, Yu-Lun Liu, ICLR, 2025 (under review)
project page / arXiv / code / demo This paper introduces a novel zero-shot video restoration method using pre-trained image restoration diffusion models, achieving excellent performance across diverse datasets and extreme video degradations. |
Project |
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DreamMesh
Ting-Hsuan Chen, Cameron Smith, Jiageng Mao, Daniel Wang, github, 2025 A Blender plugin that transforms text or image inputs into complete 3D scenes with rigged objects, realistic AI-generated backgrounds, and intelligent object placement, all powered by generative AI. |
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GPU-SplineTransformer
Ting-Hsuan Chen github, 2022 My GPU-Optimized SplineTransformer significantly accelerates the conversion of large data arrays into B-spline bases by leveraging GPU power. This innovation outperforms traditional CPU-based solutions, offering enhanced speed and efficiency for your data processing needs. |
Patent |
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Data Analysis Method, Apparatus, Electronic Device and Storage Medium
Ting-Hsuan Chen TW113141155, 2024 A data analysis method based on feature waveform analysis, improving the accuracy of feature waveform recognition through convolution and segmentation operations. This patent is currently in the confidential stage and is expected to be declassified in 2026. |
Professional Experience |
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MS Student
USC Geometry, Vision, and Learning Lab, Los Angeles, California, USA Aug 2024 - Present At USC's Geometry, Vision, and Learning Lab, I explore diffusion models as priors to improve 3D reconstruction quality and enable 3D scene editing. I also work on autonomous web agents and develop physics-based robotics simulators to bridge the sim-to-real gap. |
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Scene Understanding/GenAI Research Intern
Bosch Center for Artificial Intelligence, Sunnyvale, California, USA May 2025 - Present Working on cutting-edge research in scene understanding and generative AI technologies. Collaborating with the research team to develop innovative solutions for computer vision applications. |
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Research Assistant
NYCU Computational Photography Lab, Hsinchu, Taiwan Jan 2024 - June 2024 At the NYCU Computational Photography Lab, my primary research focused on diffusion models. During this period, I successfully published a paper as the first author, which was accepted at NeurIPS 2024. Additionally, I participated in industry-academia collaborations with Nvidia and MediaTek, applying research findings to real-world industry challenges. |
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R&D Engineer
Foxconn, Hon Hai Precision Industry, Taipei, Taiwan July 2023 - Dec 2023 At Foxconn, I developed the company's first patented ECG waveform recognition system by integrating AI, computer vision, and signal processing techniques. I also mentored new interns and represented the company in various medical conferences. Previously, I built an AI-based data cleansing and classification system, along with essential APIs using Django for medical data processing. |
Awards & Recognitions |
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