6 citations · 20 across the 8 of their papers we have counts for
9 papers
Mind-to-Face: Neural-Driven Photorealistic Avatar Synthesis via EEG Decoding
Haolin Xiong, Tianwen Fu, Pratusha Bhuvana Prasad +5
Current expressive avatar systems rely heavily on visual cues, failing when faces are occluded or when emotions remain internal. We present Mind-to-Face, the first framework that d…
Acquisition of Spatially-Varying Reflectance and Surface Normals via Polarized Reflectance Fields
Jing Yang, Pratusha Bhuvana Prasad, Qing Zhang +1
Accurately measuring the geometry and spatially-varying reflectance of real-world objects is a complex task due to their intricate shapes formed by concave features, hollow engravi…
Localized Gaussian Splatting Editing with Contextual Awareness
Hanyuan Xiao, Yingshu Chen, Huajian Huang +4
Recent text-guided generation of individual 3D object has achieved great success using diffusion priors. However, these methods are not suitable for object insertion and replacemen…
Leveraging Synthetic Data for Generalizable and Fair Facial Action Unit Detection
Liupei Lu, Yufeng Yin, Yuming Gu +4
Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is…
Ground material classification for UAV-based photogrammetric 3D data A 2D-3D Hybrid Approach
Meida Chen, Andrew Feng, Yu Hou +3
In recent years, photogrammetry has been widely used in many areas to create photorealistic 3D virtual data representing the physical environment. The innovation of small unmanned…
Semantic Segmentation and Data Fusion of Microsoft Bing 3D Cities and Small UAV-based Photogrammetric Data
Meida Chen, Andrew Feng, Kyle McCullough +3
With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing Maps team has created over 125 highly detailed 3D cities from 11 different countries that cover hundred…
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models
Meida Chen, Andrew Feng, Kyle McCullough +3
At I/ITSEC 2019, the authors presented a fully-automated workflow to segment 3D photogrammetric point-clouds/meshes and extract object information, including individual tree locati…
Fully Automated Photogrammetric Data Segmentation and Object Information Extraction Approach for Creating Simulation Terrain
Meida Chen, Andrew Feng, Kyle McCullough +4
Our previous works have demonstrated that visually realistic 3D meshes can be automatically reconstructed with low-cost, off-the-shelf unmanned aerial systems (UAS) equipped with c…
Learning Formation of Physically-Based Face Attributes
Ruilong Li, Karl Bladin, Yajie Zhao +8
Based on a combined data set of 4000 high resolution facial scans, we introduce a non-linear morphable face model, capable of producing multifarious face geometry of pore-level res…