I am committed to advancing virtual reality technology, and my current research interests are in building realistic and interactive virtual humans in real-time in virtual reality environments to enhance the user's immersive experience. My research interests also cover computer graphics, computer animation, computer vision and crowd simulation.
France
Nov. 2020 - Apr. 2024
Funded by the European Union's Marie Skłodowska-Curie Actions grant under the CLIPE-ITN project, my PhD research focused on prooposing a novel crowd motion data collection paradigm using Virtual Reality. This research experiences consisted of designing and implementing VR environments, performing user experiment, the use of VR and motion capture hardware, the use of crowd simulation algorithm such as RVO and ORCA, data analysis, and scientific writing and publication.
The One-Man-Crowd: Towards Single-User Capture of Collective Motions using Virtual Reality [PDF] [Defense]
Programing: Unity, C#, Matlab, C++, OpenCV, Python.
Technique: Xsens, Pimax, HTC Vive, HP VR backpacks
Others: scientific writing, writing of ethic proposal.
Nov. 2023
This visit happened within the CLIPE-ITN project at the end of my PhD. Hosted by my co-supervisor, the visit was mainly about making echanges about VR-experiments and user-studies.
France
France
Nov. 2022 - Jan. 2023
This visit happened within the CLIPE-ITN project. A first-step exploration of how the One-Man-Crowd dataset can serve motion generation and motion style transfer. We trained a simple auto-encoder with 6 people’s OMC data and successfully tested it on 2 unseen people’s data.
Programing: Python, PyTorch
Feb. 2022 - May. 2022
This visit happened within the CLIPE-ITN project. The goal of this visit is to setup a VR Motion Capture solution for the department’s amazing capture hull that record and replay human motion directly through the SMPLX model.
Technique: Vicon Motion Capture System.
Germany
China
Sep. 2019 - Jun. 2022
Under my master's research project, this experience happened at the State Key Laboratory of Virtual Reality technology and System. The reserach topic is reconstructing human hand shape from depth map input using a graph convolutional network (GCN). The network contains a stacked hourglass network to predict hand joints on the depth map, and a GCN to predict the hand shape.
Programing: C++, OpenGL, OpenCV, CUDA, Python, PyTorch
Technique: Depth cameras such as Azure Kinect and Intel Realsense
University of Rennes, France
Beihang University
Ecole Centrale de Marseille (Now became Ecole Centrale Méditerranée)
Beihang University
Sagrada Familia, Spain
Forbidden City, China
Rennes, France
Forbidden City, China
Château Chambourg, France
Zhangye National Geological Park, China
Lago di Garda, Italy
A PDF web editor open sourced by ShizukuIchi on Github. I deployed a copy on my own server for test purpose. This will be later removed.
This is a small and preliminary test function for an upcoming project that aims to support touristic aurora observation. This will be removed when a further step is ready.
Your location will appear here.