Dr. Pengpeng Hu is the lead of the AI4Engineering Lab. His research interests include AI for engineering, point cloud processing, biometrics, 3D human shape reconstruction, and measurement.
Interests
- AI for Engineering
- Biometrics
- Measurement
- 3D body scanning
- Ergonomics
Education
PhD in Engineering Sciences, 2022
Vrije Universiteit Brussel, Belgium
PhD in Engineering Scienc, 2017
Donghua University, China
Latest
- Bringing the queen mother of the west to life: Digital reconstruction and analysis of Taoist Celestial Beings Worshiping mural’s apparel
- Attention-Based Multi-Kernelized and Boundary-Aware Network for image semantic segmentation
- Industrially Scalable Textile Sensing Interfaces for Extended Artificial Tactile and Human Motion Monitoring without Compromising Comfort
- KD-Net: Continuous-Keystroke-Dynamics-Based Human Identification from RGB-D Image Sequences
- W2H-Net: Fast Prediction of Waist-to-Hip Ratio from Single Partial Dressed Body Scans in Arbitrary Postures via Deep Learning
- Measure4dhand: Dynamic Hand Measurement Extraction from 4D Scans
- PatientHandNet: 3D Open-palm Hand Reconstruction from Sparse Multi-view Depth Images
- Automatic Detection of Coil Position in the Chest X-ray Images for Assessing the Risks of Lead Extraction Procedures
- Point2PartVolume: Human Body Volume Estimation from A Single Depth Image
- PoseNormNet: Identity-preserved Posture Normalization of 3D Body Scans in Arbitrary Postures
- A Deep-learning-based Approach to Automatically Measuring Foots from a 3D scan
- AlignBodyNet: Deep Learning-Based Alignment of Non-Overlapping Partial Body Point Clouds From a Single Depth Camera
- Automatic Foot Measurement Extraction from A 3D Point Cloud via a Deep Neural Network
- OrienNormNet: Orientation Normalization of 3D Body Models
- Automatic and fast extraction of 3d hand measurements using a deep neural network
- A deep learning approach to automatically extract 3d hand measurements
- Predicting high-fidelity human body models from impaired point clouds
- A pose-based feature fusion and classification framework for the early prediction of cerebral palsy in infants
- Anet: A deep neural network for automatic 3d anthropometric measurement extraction
- Deep learning-based automated extraction of anthropometric measurements from a single 3D scan
- MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets
- 3DBodyNet: fast reconstruction of 3D animatable human body shape from a single commodity depth camera
- Method for registration of 3D shapes without overlap for known 3D priors
- Learning to estimate the body shape under clothing from a single 3-D scan
- Multi‐modal deep network for RGB‐D segmentation of clothes
- A generic method of wearable items virtual try-on
- A new method to evaluate the dynamic air gap thickness and garment sliding of virtual clothes during walking
- Automatic three-dimensional-scanned garment fitting based on virtual tailoring and geometric sewing
- Automatic body measurement based on slicing loops
- Personalized 3D mannequin reconstruction based on 3D scanning
- 3D textile reconstruction based on KinectFusion and synthesized texture
- A study on improving the calibration of body scanner built on multiple RGB-Depth cameras
- Scanning and animating characters dressed in multiple-layer garments
- Automatic foot scanning and measurement based on multiple RGB-depth cameras