1 code implementation • 5 May 2023 • Nicholas Sharp, Cristian Romero, Alec Jacobson, Etienne Vouga, Paul G. Kry, David I. W. Levin, Justin Solomon
Physical systems ranging from elastic bodies to kinematic linkages are defined on high-dimensional configuration spaces, yet their typical low-energy configurations are concentrated on much lower-dimensional subspaces.
no code implementations • CVPR 2022 • Hsiao-yu Chen, Edgar Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner
We present Virtual Elastic Objects (VEOs): virtual objects that not only look like their real-world counterparts but also behave like them, even when subject to novel interactions.
1 code implementation • ICCV 2021 • Siming Yan, Zhenpei Yang, Chongyang Ma, Haibin Huang, Etienne Vouga, QiXing Huang
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
no code implementations • NeurIPS 2020 • Xiangru Huang, Haitao Yang, Etienne Vouga, QiXing Huang
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.
no code implementations • 6 Aug 2018 • Zaiwei Zhang, Zhenpei Yang, Chongyang Ma, Linjie Luo, Alexander Huth, Etienne Vouga, Qi-Xing Huang
We show a principled way to train this model by combining discriminator losses for both a 3D object arrangement representation and a 2D image-based representation.
no code implementations • 19 Apr 2018 • Cem C. Tutum, Supawit Chockchowwat, Etienne Vouga, Risto Miikkulainen
The proposed methodology for discovering solutions to this problem consists of three components: First, an effective search space is learned through a variational autoencoder (VAE); second, a surrogate model for functional designs is built; and third, a genetic algorithm is used to simultaneously update the hyperparameters of the surrogate and to optimize the designs using the updated surrogate.
no code implementations • 11 Apr 2016 • Ruizhe Wang, Lingyu Wei, Etienne Vouga, Qi-Xing Huang, Duygu Ceylan, Gerard Medioni, Hao Li
We present an end-to-end system for reconstructing complete watertight and textured models of moving subjects such as clothed humans and animals, using only three or four handheld sensors.
no code implementations • CVPR 2016 • Lingyu Wei, Qi-Xing Huang, Duygu Ceylan, Etienne Vouga, Hao Li
We propose a deep learning approach for finding dense correspondences between 3D scans of people.