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Found 7 papers, 0 papers with code
A Neural-Network-Based Approach for Loose-Fitting Clothing
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 with a real-time numerical algorithm specifically designed to mimic the most important ballistic features of a classical numerical simulation.
Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks
We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks.
Skinning a Parameterization of Three-Dimensional Space for Neural Network Cloth
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 underlying body.
Recovering Geometric Information with Learned Texture Perturbations
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-frequency information present in the training data.
Coercing Machine Learning to Output Physically Accurate Results
Although one could project the output of a network into a physically feasible region, such a postprocess is not captured by the energy function minimized when training the network; thus, the final projected result could incorrectly deviate quite far from the training data.
3D Guided Fine-Grained Face Manipulation
This is achieved by first fitting a 3D face model and then disentangling the face into a texture and a shape.
A Pixel-Based Framework for Data-Driven Clothing
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 as an RGB image in a two dimensional pattern space.
