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20182026
most citedCoercing Machine Learning to Output Physically Accurate Results

37 citations · 63 across the 13 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

Detangled: A Framework for Creating, Editing, and Inferencing Feature Rich Hair Strands

Sarah Jobalia, Yitong Deng, Carolyn Smith +1

We present a framework for understanding and generating feature rich hair strands. Drawing upon both scientific and cultural expertise, we define strand texture as the various dist…

cs.CV2022

Leveraging Deepfakes to Close the Domain Gap between Real and Synthetic Images in Facial Capture Pipelines

Winnie Lin, Yilin Zhu, Demi Guo +1

We propose an end-to-end pipeline for both building and tracking 3D facial models from personalized in-the-wild (cellphone, webcam, youtube clips, etc.) video data. First, we prese…

cs.CV2020

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…

cs.CV2020★ 3 cited

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…

cs.CV2019★ 1 cited

Local Geometric Indexing of High Resolution Data for Facial Reconstruction from Sparse Markers

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 smooth…

cs.CV2018

Three Dimensional Reconstruction of Botanical Trees with Simulatable Geometry

Ed Quigley, Winnie Lin, Yilin Zhu +1

We tackle the challenging problem of creating full and accurate three dimensional reconstructions of botanical trees with the topological and geometric accuracy required for subseq…

cs.CV2018

Deep Energies for Estimating Three-Dimensional Facial Pose and Expression

Michael Bao, Jane Wu, Xinwei Yao +1

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 rotosco…

cs.CV2018★ 1 cited

High-Quality Face Capture Using Anatomical Muscles

Michael Bao, Matthew Cong, Stéphane Grabli +1

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 diff…

cs.CV2018★ 18 cited

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…

cs.CV2018★ 2 cited

Improved Search Strategies with Application to Estimating Facial Blendshape Parameters

Michael Bao, David Hyde, Xinru Hua +1

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 term…