3 citations · 5 across the 4 of their papers we have counts for
4 papers
A Neural-Network-Based Approach for Loose-Fitting Clothing
Yongxu Jin, Dalton Omens, Zhenglin Geng +4
Since loose-fitting clothing contains dynamic modes that have proven to be difficult to predict via neural networks, we first illustrate how to coarsely approximate these modes wit…
Software-based Automatic Differentiation is Flawed
Daniel Johnson, Trevor Maxfield, Yongxu Jin +1
Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation v…
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…
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…