no code implementations • 25 Apr 2024 • Yongxu Jin, Dalton Omens, Zhenglin Geng, Joseph Teran, Abishek Kumar, Kenji Tashiro, Ronald Fedkiw
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.
no code implementations • 27 Nov 2023 • Jane Wu, Diego Thomas, Ronald Fedkiw
We present a novel deep learning-based approach to the 3D reconstruction of clothed humans using weak supervision via 2D normal maps.
no code implementations • 21 Jun 2023 • Daniel Johnson, Ronald Fedkiw
There are many physical processes that have inherent discontinuities in their mathematical formulations.
no code implementations • 5 May 2023 • Daniel Johnson, Trevor Maxfield, Yongxu Jin, Ronald Fedkiw
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.
no code implementations • 25 Jan 2022 • Yongxu Jin, Yushan Han, Zhenglin Geng, Joseph Teran, Ronald Fedkiw
We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks.
no code implementations • 8 Jun 2020 • Jane Wu, Zhenglin Geng, Hui Zhou, Ronald Fedkiw
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.
no code implementations • 20 Jan 2020 • Jane Wu, Yongxu Jin, Zhenglin Geng, Hui Zhou, Ronald Fedkiw
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.
no code implementations • 21 Oct 2019 • Zhenglin Geng, Dan Johnson, Ronald Fedkiw
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.
no code implementations • 1 Mar 2019 • 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 smoothness constraints.
no code implementations • 20 Dec 2018 • Ed Quigley, Winnie Lin, Yilin Zhu, Ronald Fedkiw
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.
no code implementations • 7 Dec 2018 • Michael Bao, Jane Wu, Xinwei Yao, Ronald Fedkiw
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.
no code implementations • 7 Dec 2018 • Michael Bao, David Hyde, Xinru Hua, Ronald Fedkiw
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.
no code implementations • CVPR 2019 • Michael Bao, Matthew Cong, Stéphane Grabli, Ronald Fedkiw
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.
no code implementations • 3 Dec 2018 • Ning Jin, Yilin Zhu, Zhenglin Geng, Ronald Fedkiw
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.