Graph Representation Learning
Graph Neural Networks (GNNs) and Graph Convolutional Networks (GCNs) generalize the power of CNN to learn on graph-structured data (e.g., citation networks, molecules/proteins, 3D meshes). However, existing GNNs cannot be trained deeply and has a limited application range. Our research line includes both theoretical analysis of GNNs and exploration for their potential applications:
Neural Representation and Optimization
An emerging perspective towards neural networks is to treat them as universal approximators of arbitrary continuous functions. One can solve many inverse problems by: 1) representing the objective function as a multi-layer perceptron, 2) writing a differentiable forward process, and 3) adopting gradient-based algorithms to reach the optimum. In particular, we are using neural representation framework to solve inter-disciplinary challenges in physical imaging, medical imaging, and biological microscopy:
3D Human Avatar Modeling
Human avatars are one of the most important elements in virtual reality. Our research line includes 3D human body reconstruction, 3D human clothing reconstruction/simulation, 3D pose estimation, human motion capture, and action understanding:
Towards Competitive Diving Video Understanding
Submitted to ECCV 2020
Competitive diving video understanding by identifying the dive number, assessing the performance, 2D pose/action recovery.
3D Clothing Segmentation via Graph Convolutional Networks
Garment segmentation networks based on graph convolutional networks for human models.
Towards 3D Human Shape Recovery Under Clothing
Submitted to ICCV 2019
A learning-based scheme for robustly and accurately estimating clothing fitness as well as the human shape on clothed 3D human scans.
Keep thy heart with all diligence; for out of it are the issues of life.
I was born and raised up in Chengdu, southwestern China, together with my parents and my brother.
I learned C/C++ programming languages at 12 years old, and spent my leisure time developing myBase Desktop 7 during high school.
I am now in Shanghai, eastern China to pursue my Bachelor's degree at ShanghaiTech University with a focus on machine learning and computer vision. I enjoy doing research, reading insightful papers, and writing computer programs. I believe good work should deliver either mathematical motivations or extensive engineering efforts.
I am also a core team memember of GeekPie Association, with whom I played Hackthathon and took part in lots of innovation and development design-sprint events.