Search Results for author: Etienne Vouga

Found 7 papers, 1 papers with code

Virtual Elastic Objects

no code implementations12 Jan 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.

HPNet: Deep Primitive Segmentation Using Hybrid Representations

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.

Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs

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.

Deep Generative Modeling for Scene Synthesis via Hybrid Representations

no code implementations6 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.

Functional Generative Design: An Evolutionary Approach to 3D-Printing

no code implementations19 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.

Capturing Dynamic Textured Surfaces of Moving Targets

no code implementations11 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.

Dense Human Body Correspondences Using Convolutional Networks

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.

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