3 citations · 3 across the 4 of their papers we have counts for
12 papers
Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines
Winnie Lin, Yilin Zhu, Demi Guo +1
We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.) video data. First, we prese…
Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks
Yongxu Jin, Yushan Han, Zhenglin Geng +2
We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. In order to significantly reduce the requirements on data…
Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
Jane Wu, Zhenglin Geng, Hui Zhou +1
We present a novel learning framework for cloth deformation by embedding virtual cloth into a tetrahedral mesh that parametrizes the volumetric region of air surrounding the underl…
Recovering Geometric Information with Learned Texture Perturbations
Jane Wu, Yongxu Jin, Zhenglin Geng +2
Regularization is used to avoid overfitting when training a neural network; unfortunately, this reduces the attainable level of detail hindering the ability to capture high-frequen…
Coercing Machine Learning to Output Physically Accurate Results
Zhenglin Geng, Dan Johnson, Ronald Fedkiw
Many machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data…
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…