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Found 13 papers, 0 papers with code
Weakly-Supervised 3D Reconstruction of Clothed Humans via Normal Maps
We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps.
Addressing Discontinuous Root-Finding for Subsequent Differentiability in Machine Learning, Inverse Problems, and Control
There are many physical processes that have inherent discontinuities in their mathematical formulations.
Software-based Automatic Differentiation is Flawed
Various software efforts embrace the idea that object oriented programming enables a convenient implementation of the chain rule, facilitating so-called automatic differentiation via backpropagation.
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.
Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers
When considering sparse motion capture marker data, one typically struggles to balance its overfitting via a high dimensional blendshape system versus underfitting caused by smoothness constraints.
Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry
We tackle the challenging problem of creating full and accurate three dimensional reconstructions of botanical trees with the topological and geometric accuracy required for subsequent physical simulation, e. g. in response to wind forces.
Deep Energies for Estimating Three-Dimensional Facial Pose and Expression
While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image.
Improved Search Strategies with Application to Estimating Facial Blendshape Parameters
It is well known that popular optimization techniques can lead to overfitting or even a lack of convergence altogether; thus, practitioners often utilize ad hoc regularization terms added to the energy functional.
High-Quality Face Capture Using Anatomical Muscles
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 differentiability has limited their impact.
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.
