Search Results for author: Thibault Groueix

Found 24 papers, 13 papers with code

TutteNet: Injective 3D Deformations by Composition of 2D Mesh Deformations

no code implementations CVPR 2024 Bo Sun, Thibault Groueix, Chen Song, QiXing Huang, Noam Aigerman

This work proposes a novel representation of injective deformations of 3D space, which overcomes existing limitations of injective methods: inaccuracy, lack of robustness, and incompatibility with general learning and optimization frameworks.

Learning Continuous 3D Words for Text-to-Image Generation

no code implementations CVPR 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.

Text-to-Image Generation

Generative Escher Meshes

1 code implementation25 Sep 2023 Noam Aigerman, Thibault Groueix

We prove that the solution space of these linear systems is exactly all possible valid tiling configurations, thereby providing an end-to-end differentiable representation for the entire space of valid tiles.

valid

PSDR-Room: Single Photo to Scene using Differentiable Rendering

no code implementations6 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.

Scene Understanding

Neural Face Rigging for Animating and Retargeting Facial Meshes in the Wild

1 code implementation15 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.

TextDeformer: Geometry Manipulation using Text Guidance

1 code implementation26 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.

NOPE: Novel Object Pose Estimation from a Single Image

1 code implementation CVPR 2024 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.

Object Pose Estimation

PoseBERT: A Generic Transformer Module for Temporal 3D Human Modeling

1 code implementation22 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.

Pose Estimation Pose Prediction

Learning Joint Surface Atlases

no code implementations13 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.

Neural Jacobian Fields: Learning Intrinsic Mappings of Arbitrary Meshes

1 code implementation5 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.

Deep Transformation-Invariant Clustering

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)

Clustering Image Clustering +1

Learning elementary structures for 3D shape generation and matching

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)

3D Dense Shape Correspondence 3D Shape Generation +1

3D-CODED: 3D Correspondences by Deep Deformation

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.

3D-CODED : 3D Correspondences by Deep Deformation

1 code implementation13 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)

3D Dense Shape Correspondence 3D Human Pose Estimation +2

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