25 citations · 31 across the 7 of their papers we have counts for
7 papers
A Scalable PyTorch Abstraction for Multi-GPU Gaussian Splatting
Matthew Cong, Francis Williams, Jonathan Swartz +3
Gaussian splatting methods have become increasingly popular for neural reconstruction of the real world. However, they are often limited in scale and resolution due to compute and…
An Interface Tracking Method with Triangle Edge Cuts
Mengdi Wang, Matthew Cong, Bo Zhu
This paper introduces a volume-conserving interface tracking algorithm on unstructured triangle meshes. We propose to discretize the interface via triangle edge cuts which represen…
fVDB: A Deep-Learning Framework for Sparse, Large-Scale, and High-Performance Spatial Intelligence
Francis Williams, Jiahui Huang, Jonathan Swartz +9
We present fVDB, a novel GPU-optimized framework for deep learning on large-scale 3D data. fVDB provides a complete set of differentiable primitives to build deep learning architec…
Near-realtime Facial Animation by Deep 3D Simulation Super-Resolution
Hyojoon Park, Sangeetha Grama Srinivasan, Matthew Cong +5
We present a neural network-based simulation super-resolution framework that can efficiently and realistically enhance a facial performance produced by a low-cost, realtime physics…
Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers
Matthew Cong, Lana Lan, Ronald Fedkiw
When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smooth…
High-Quality Face Capture Using Anatomical Muscles
Michael Bao, Matthew Cong, Stéphane Grabli +1
Muscle-based systems have the potential to provide both anatomical accuracy and semantic interpretability as compared to blendshape models; however, a lack of expressivity and diff…