1 code implementation • 15 Oct 2024 • Nikita Karaev, Iurii Makarov, Jianyuan Wang, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
Most state-of-the-art point trackers are trained on synthetic data due to the difficulty of annotating real videos for this task.
no code implementations • 2 Jul 2024 • Raphael Bensadoun, Yanir Kleiman, Idan Azuri, Omri Harosh, Andrea Vedaldi, Natalia Neverova, Oran Gafni
The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects.
no code implementations • 2 Jul 2024 • Yawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya, Yanir Kleiman, Emilien Garreau, Oran Gafni, Natalia Neverova, Andrea Vedaldi, Roman Shapovalov, David Novotny
We present Meta 3D AssetGen (AssetGen), a significant advancement in text-to-3D generation which produces faithful, high-quality meshes with texture and material control.
no code implementations • 2 Jul 2024 • Raphael Bensadoun, Tom Monnier, Yanir Kleiman, Filippos Kokkinos, Yawar Siddiqui, Mahendra Kariya, Omri Harosh, Roman Shapovalov, Benjamin Graham, Emilien Garreau, Animesh Karnewar, Ang Cao, Idan Azuri, Iurii Makarov, Eric-Tuan Le, Antoine Toisoul, David Novotny, Oran Gafni, Natalia Neverova, Andrea Vedaldi
We introduce Meta 3D Gen (3DGen), a new state-of-the-art, fast pipeline for text-to-3D asset generation.
no code implementations • 13 Feb 2024 • Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, Natalia Neverova, Andrea Vedaldi, Oran Gafni, Filippos Kokkinos
A mitigation is to fine-tune the 2D generator to be multi-view aware, which can help distillation or can be combined with reconstruction networks to output 3D objects directly.
no code implementations • CVPR 2024 • Lior Yariv, Omri Puny, Natalia Neverova, Oran Gafni, Yaron Lipman
Current diffusion or flow-based generative models for 3D shapes divide to two: distilling pre-trained 2D image diffusion models, and training directly on 3D shapes.
1 code implementation • ICCV 2023 • Roman Shapovalov, Yanir Kleiman, Ignacio Rocco, David Novotny, Andrea Vedaldi, Changan Chen, Filippos Kokkinos, Ben Graham, Natalia Neverova
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
1 code implementation • 14 Jul 2023 • Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
We introduce CoTracker, a transformer-based model that tracks a large number of 2D points in long video sequences.
Ranked #2 on Point Tracking on TAP-Vid-Kinetics-First
1 code implementation • CVPR 2023 • Nikita Karaev, Ignacio Rocco, Benjamin Graham, Natalia Neverova, Andrea Vedaldi, Christian Rupprecht
The network learns to pool information from neighboring frames to improve the temporal consistency of its predictions.
1 code implementation • 21 Mar 2023 • Ignacio Rocco, Iurii Makarov, Filippos Kokkinos, David Novotny, Benjamin Graham, Natalia Neverova, Andrea Vedaldi
We present a method for fast 3D reconstruction and real-time rendering of dynamic humans from monocular videos with accompanying parametric body fits.
no code implementations • CVPR 2023 • Changan Chen, Alexander Richard, Roman Shapovalov, Vamsi Krishna Ithapu, Natalia Neverova, Kristen Grauman, Andrea Vedaldi
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint?
1 code implementation • 6 Dec 2022 • Mohamed El Banani, Ignacio Rocco, David Novotny, Andrea Vedaldi, Natalia Neverova, Justin Johnson, Benjamin Graham
To address this, we propose a self-supervised approach for correspondence estimation that learns from multiview consistency in short RGB-D video sequences.
no code implementations • CVPR 2023 • Samarth Sinha, Roman Shapovalov, Jeremy Reizenstein, Ignacio Rocco, Natalia Neverova, Andrea Vedaldi, David Novotny
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
no code implementations • ICLR 2022 • Steeven Janny, Fabien Baradel, Natalia Neverova, Madiha Nadri, Greg Mori, Christian Wolf
Learning causal relationships in high-dimensional data (images, videos) is a hard task, as they are often defined on low dimensional manifolds and must be extracted from complex signals dominated by appearance, lighting, textures and also spurious correlations in the data.
no code implementations • CVPR 2022 • David Novotny, Ignacio Rocco, Samarth Sinha, Alexandre Carlier, Gael Kerchenbaum, Roman Shapovalov, Nikita Smetanin, Natalia Neverova, Benjamin Graham, Andrea Vedaldi
Compared to weaker deformation models, this significantly reduces the reconstruction ambiguity and, for dynamic objects, allows Keypoint Transporter to obtain reconstructions of the quality superior or at least comparable to prior approaches while being much faster and reliant on a pre-trained monocular depth estimator network.
1 code implementation • CVPR 2022 • Gengshan Yang, Minh Vo, Natalia Neverova, Deva Ramanan, Andrea Vedaldi, Hanbyul Joo
Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to gradient-based optimization, and (3) canonical embeddings that generate correspondences between pixels and an articulated model.
no code implementations • CVPR 2021 • Marvin Eisenberger, David Novotny, Gael Kerchenbaum, Patrick Labatut, Natalia Neverova, Daniel Cremers, Andrea Vedaldi
We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes and produces in one go, i. e. in a single feed forward pass, a smooth interpolation and point-to-point correspondences between them.
11 code implementations • NeurIPS 2021 • Alaaeldin El-Nouby, Hugo Touvron, Mathilde Caron, Piotr Bojanowski, Matthijs Douze, Armand Joulin, Ivan Laptev, Natalia Neverova, Gabriel Synnaeve, Jakob Verbeek, Hervé Jegou
We propose a "transposed" version of self-attention that operates across feature channels rather than tokens, where the interactions are based on the cross-covariance matrix between keys and queries.
Ranked #57 on Instance Segmentation on COCO minival
no code implementations • CVPR 2021 • Natalia Neverova, Artsiom Sanakoyeu, Patrick Labatut, David Novotny, Andrea Vedaldi
Recent work has shown that it is possible to learn a unified dense pose predictor for several categories of related objects.
no code implementations • Findings (EMNLP) 2021 • Shir Gur, Natalia Neverova, Chris Stauffer, Ser-Nam Lim, Douwe Kiela, Austin Reiter
Recent advances in using retrieval components over external knowledge sources have shown impressive results for a variety of downstream tasks in natural language processing.
1 code implementation • 10 Feb 2021 • Alaaeldin El-Nouby, Natalia Neverova, Ivan Laptev, Hervé Jégou
Transformers have shown outstanding results for natural language understanding and, more recently, for image classification.
1 code implementation • NeurIPS 2020 • Natalia Neverova, David Novotny, Vasil Khalidov, Marc Szafraniec, Patrick Labatut, Andrea Vedaldi
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.
1 code implementation • 7 Apr 2020 • Hanbyul Joo, Natalia Neverova, Andrea Vedaldi
Remarkably, the resulting annotations are sufficient to train from scratch 3D pose regressor networks that outperform the current state-of-the-art on in-the-wild benchmarks such as 3DPW.
Ranked #23 on 3D Human Pose Estimation on MPI-INF-3DHP (PA-MPJPE metric)
1 code implementation • CVPR 2020 • Artsiom Sanakoyeu, Vasil Khalidov, Maureen S. McCarthy, Andrea Vedaldi, Natalia Neverova
Recent contributions have demonstrated that it is possible to recognize the pose of humans densely and accurately given a large dataset of poses annotated in detail.
no code implementations • NeurIPS 2019 • Natalia Neverova, David Novotny, Andrea Vedaldi
We show that these models, by understanding uncertainty better, can solve the original DensePose task more accurately, thus setting the new state-of-the-art accuracy in this benchmark.
1 code implementation • ICLR 2020 • Fabien Baradel, Natalia Neverova, Julien Mille, Greg Mori, Christian Wolf
Understanding causes and effects in mechanical systems is an essential component of reasoning in the physical world.
2 code implementations • ICCV 2019 • David Novotny, Nikhila Ravi, Benjamin Graham, Natalia Neverova, Andrea Vedaldi
We propose C3DPO, a method for extracting 3D models of deformable objects from 2D keypoint annotations in unconstrained images.
no code implementations • CVPR 2019 • Natalia Neverova, James Thewlis, Riza Alp Güler, Iasonas Kokkinos, Andrea Vedaldi
DensePose supersedes traditional landmark detectors by densely mapping image pixels to body surface coordinates.
no code implementations • ECCV 2018 • Natalia Neverova, Riza Alp Guler, Iasonas Kokkinos
In this work we integrate ideas from surface-based modeling with neural synthesis: we propose a combination of surface-based pose estimation and deep generative models that allows us to perform accurate pose transfer, i. e. synthesize a new image of a person based on a single image of that person and the image of a pose donor.
1 code implementation • ECCV 2018 • Fabien Baradel, Natalia Neverova, Christian Wolf, Julien Mille, Greg Mori
Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context.
Ranked #1 on Semantic Object Interaction Classification on VLOG
22 code implementations • CVPR 2018 • Riza Alp Güler, Natalia Neverova, Iasonas Kokkinos
In this work, we establish dense correspondences between RGB image and a surface-based representation of the human body, a task we refer to as dense human pose estimation.
Ranked #2 on Pose Estimation on DensePose-COCO
no code implementations • NeurIPS 2017 • Moustapha M. Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet
Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines.
no code implementations • 12 Aug 2017 • Natalia Neverova, Iasonas Kokkinos
Despite the large improvements in performance attained by using deep learning in computer vision, one can often further improve results with some additional post-processing that exploits the geometric nature of the underlying task.
no code implementations • 17 Jul 2017 • Moustapha Cisse, Yossi Adi, Natalia Neverova, Joseph Keshet
Generating adversarial examples is a critical step for evaluating and improving the robustness of learning machines.
2 code implementations • ICCV 2017 • Pauline Luc, Natalia Neverova, Camille Couprie, Jakob Verbeek, Yann Lecun
The ability to predict and therefore to anticipate the future is an important attribute of intelligence.
no code implementations • 20 Nov 2015 • Natalia Neverova, Christian Wolf, Florian Nebout, Graham Taylor
We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input.
no code implementations • 12 Nov 2015 • Natalia Neverova, Christian Wolf, Griffin Lacey, Lex Fridman, Deepak Chandra, Brandon Barbello, Graham Taylor
We present a large-scale study exploring the capability of temporal deep neural networks to interpret natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors.
no code implementations • 31 Dec 2014 • Natalia Neverova, Christian Wolf, Graham W. Taylor, Florian Nebout
We present a method for gesture detection and localisation based on multi-scale and multi-modal deep learning.