Guan Ming Lim

I'm a researcher at Rehabilitation Research Institute of Singapore (RRIS) Nanyang Technological University (NTU), where I work on markerless mocap of hand and object tracking.

Google Scholar  /  Github  /  YouTube

I'm interested in combining research on computer vision, computer graphics and machine learning to understand human motion. Much of my research is on hand tracking from images.

Real-time, accurate, and robust tracking of a rigid object using image-based methods such as efficient projective point correspondence and precomputed spare viewpoint information.

A low-cost, modular, and wireless pressure sensor array that can generate real-time pressure distribution map for object pose estimation and grasp classification.

Real-time estimation of 3D hand shape and pose from a single RGB image running at over 110 Hz on a GPU or 75 Hz on a CPU.

A camera-based system for markerless hand pose estimation using a mirror-based multi-view setup to eliminate the complexity of synchronizing multiple cameras and reduce the issue of occlusion.

Semantic segmentation of body parts (e.g. hand and arm) and objects from depth image. A Fully Convolutional Neural Network is trained on synthetic data with some level of generalization on real data.

SDF-Net is a simple multilayer perceptron (with a memory footprint of < 10 kB) that is used to model the signed distance function (SDF) of a rigid object for real-time tracking (≈ 1.29 ms per frame on 1 CPU core) of the object using a single depth camera.

Design and source code from Jon Barron's website