3 citations · 4 across the 3 of their papers we have counts for
6 papers
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…
3D Guided Fine-Grained Face Manipulation
Zhenglin Geng, Chen Cao, Sergey Tulyakov
We present a method for fine-grained face manipulation. Given a face image with an arbitrary expression, our method can synthesize another arbitrary expression by the same person.…
A Pixel-Based Framework for Data-Driven Clothing
Ning Jin, Yilin Zhu, Zhenglin Geng +1
With the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation a…