3 code implementations • 15 Feb 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We introduce a method for learning to generate the surface of 3D shapes.
Ranked #5 on Point Cloud Completion on Completion3D
no code implementations • CVPR 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We introduce a method for learning to generate the surface of 3D shapes.
1 code implementation • 13 Jun 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
Ranked #9 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)
no code implementations • ECCV 2018 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
By predicting this feature for a new shape, we implicitly predict correspondences between this shape and the template.
no code implementations • 9 Oct 2018 • Tomas Hodan, Rigas Kouskouridas, Tae-Kyun Kim, Federico Tombari, Kostas Bekris, Bertram Drost, Thibault Groueix, Krzysztof Walas, Vincent Lepetit, Ales Leonardis, Carsten Steger, Frank Michel, Caner Sahin, Carsten Rother, Jiri Matas
The workshop featured four invited talks, oral and poster presentations of accepted workshop papers, and an introduction of the BOP benchmark for 6D object pose estimation.
no code implementations • 6 Jul 2019 • Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We propose a self-supervised approach to deep surface deformation.
3 code implementations • NeurIPS 2019 • Theo Deprelle, Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, Mathieu Aubry
We propose to represent shapes as the deformation and combination of learnable elementary 3D structures, which are primitives resulting from training over a collection of shape.
Ranked #8 on 3D Dense Shape Correspondence on SHREC'19 (using extra training data)
1 code implementation • NeurIPS 2020 • Tom Monnier, Thibault Groueix, Mathieu Aubry
In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space.
Ranked #2 on Unsupervised Image Classification on SVHN (using extra training data)
1 code implementation • 18 Oct 2021 • Fabien Baradel, Thibault Groueix, Philippe Weinzaepfel, Romain Brégier, Yannis Kalantidis, Grégory Rogez
In fact, we show that simply fine-tuning the batch normalization layers of the model is enough to achieve large gains.
Ranked #60 on 3D Human Pose Estimation on 3DPW
1 code implementation • 5 May 2022 • Noam Aigerman, Kunal Gupta, Vladimir G. Kim, Siddhartha Chaudhuri, Jun Saito, Thibault Groueix
This paper introduces a framework designed to accurately predict piecewise linear mappings of arbitrary meshes via a neural network, enabling training and evaluating over heterogeneous collections of meshes that do not share a triangulation, as well as producing highly detail-preserving maps whose accuracy exceeds current state of the art.
no code implementations • 13 Jun 2022 • Theo Deprelle, Thibault Groueix, Noam Aigerman, Vladimir G. Kim, Mathieu Aubry
We demonstrate that this improves the quality of the learned surface representation, as well as its consistency in a collection of related shapes.
no code implementations • 1 Jul 2022 • Marissa Ramirez de Chanlatte, Matheus Gadelha, Thibault Groueix, Radomir Mech
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images.
1 code implementation • 22 Aug 2022 • Fabien Baradel, Romain Brégier, Thibault Groueix, Philippe Weinzaepfel, Yannis Kalantidis, Grégory Rogez
It is simple, generic and versatile, as it can be plugged on top of any image-based model to transform it in a video-based model leveraging temporal information.
1 code implementation • 23 Mar 2023 • Van Nguyen Nguyen, Thibault Groueix, Yinlin Hu, Mathieu Salzmann, Vincent Lepetit
The practicality of 3D object pose estimation remains limited for many applications due to the need for prior knowledge of a 3D model and a training period for new objects.
1 code implementation • 26 Apr 2023 • William Gao, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, Rana Hanocka
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
1 code implementation • 15 May 2023 • Dafei Qin, Jun Saito, Noam Aigerman, Thibault Groueix, Taku Komura
We propose an end-to-end deep-learning approach for automatic rigging and retargeting of 3D models of human faces in the wild.
no code implementations • 6 Jul 2023 • Kai Yan, Fujun Luan, Miloš Hašan, Thibault Groueix, Valentin Deschaintre, Shuang Zhao
A 3D digital scene contains many components: lights, materials and geometries, interacting to reach the desired appearance.
1 code implementation • 20 Jul 2023 • Van Nguyen Nguyen, Thibault Groueix, Georgy Ponimatkin, Vincent Lepetit, Tomas Hodan
We propose a simple three-stage approach to segment unseen objects in RGB images using their CAD models.
no code implementations • 25 Sep 2023 • Noam Aigerman, Thibault Groueix
We thus consider both the mesh's tile-shape and its texture as optimizable parameters, rendering the textured mesh via a differentiable renderer.
no code implementations • ICCV 2023 • Ta-Ying Cheng, Matheus Gadelha, Soren Pirk, Thibault Groueix, Radomir Mech, Andrew Markham, Niki Trigoni
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets.
1 code implementation • 23 Nov 2023 • Van Nguyen Nguyen, Thibault Groueix, Mathieu Salzmann, Vincent Lepetit
We present GigaPose, a fast, robust, and accurate method for CAD-based novel object pose estimation in RGB images.
no code implementations • 13 Feb 2024 • Ta-Ying Cheng, Matheus Gadelha, Thibault Groueix, Matthew Fisher, Radomir Mech, Andrew Markham, Niki Trigoni
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
no code implementations • 3 Apr 2024 • Duygu Ceylan, Valentin Deschaintre, Thibault Groueix, Rosalie Martin, Chun-Hao Huang, Romain Rouffet, Vladimir Kim, Gaëtan Lassagne
We present MatAtlas, a method for consistent text-guided 3D model texturing.