no code implementations • 15 Oct 2024 • Charlie Hewitt, Fatemeh Saleh, Sadegh Aliakbarian, Lohit Petikam, Shideh Rezaeifar, Louis Florentin, Zafiirah Hosenie, Thomas J Cashman, Julien Valentin, Darren Cosker, Tadas Baltrusaitis
In this work, we introduce the first technique for marker-free, high-quality reconstruction of the complete human body, including eyes and tongue, without requiring any calibration, manual intervention or custom hardware.
no code implementations • 23 Sep 2024 • Chen Guo, Tianjian Jiang, Manuel Kaufmann, Chengwei Zheng, Julien Valentin, Jie Song, Otmar Hilliges
Our method, ReLoo, overcomes this limitation and reconstructs high-quality 3D models of humans dressed in loose garments from monocular in-the-wild videos.
no code implementations • 6 Jun 2024 • Salvatore Esposito, Qingshan Xu, Kacper Kania, Charlie Hewitt, Octave Mariotti, Lohit Petikam, Julien Valentin, Arno Onken, Oisin Mac Aodha
We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections.
no code implementations • CVPR 2024 • Zeren Jiang, Chen Guo, Manuel Kaufmann, Tianjian Jiang, Julien Valentin, Otmar Hilliges, Jie Song
We present MultiPly, a novel framework to reconstruct multiple people in 3D from monocular in-the-wild videos.
no code implementations • 8 Dec 2023 • Jalees Nehvi, Berna Kabadayi, Julien Valentin, Justus Thies
In contrast to this, we propose a template-based tracking of the torso, head and facial expressions which allows us to cover the appearance of a human subject from all sides.
no code implementations • CVPR 2023 • Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin, Tomasz Trzciński, Marek Kowalski
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.
1 code implementation • ICCV 2023 • Sergey Prokudin, Qianli Ma, Maxime Raafat, Julien Valentin, Siyu Tang
In this work, we present a dynamic point field model that combines the representational benefits of explicit point-based graphics with implicit deformation networks to allow efficient modeling of non-rigid 3D surfaces.
1 code implementation • CVPR 2023 • Kaiyue Shen, Chen Guo, Manuel Kaufmann, Juan Jose Zarate, Julien Valentin, Jie Song, Otmar Hilliges
Our method models bodies, hands, facial expressions and appearance in a holistic fashion and can be learned from either full 3D scans or RGB-D data.
Ranked #2 on
3D Human Reconstruction
on 4D-DRESS
1 code implementation • 5 Oct 2022 • Gwangbin Bae, Martin de La Gorce, Tadas Baltrusaitis, Charlie Hewitt, Dong Chen, Julien Valentin, Roberto Cipolla, Jingjing Shen
Such models are trained on large-scale datasets that contain millions of real human face images collected from the internet.
Ranked #2 on
Synthetic Face Recognition
on CPLFW
(Accuracy metric)
no code implementations • 1 Aug 2022 • Stephan J. Garbin, Marek Kowalski, Virginia Estellers, Stanislaw Szymanowicz, Shideh Rezaeifar, Jingjing Shen, Matthew Johnson, Julien Valentin
The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time.
no code implementations • 6 Apr 2022 • Erroll Wood, Tadas Baltrusaitis, Charlie Hewitt, Matthew Johnson, Jingjing Shen, Nikola Milosavljevic, Daniel Wilde, Stephan Garbin, Chirag Raman, Jamie Shotton, Toby Sharp, Ivan Stojiljkovic, Tom Cashman, Julien Valentin
By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild.
Ranked #1 on
3D Face Reconstruction
on Florence
(RMSE Indoor metric)
no code implementations • 29 Nov 2021 • Vasileios Choutas, Federica Bogo, Jingjing Shen, Julien Valentin
A common first step in systems that tackle these problems is to regress the parameters of the parametric model directly from the input data.
1 code implementation • ICCV 2021 • Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin
Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.
1 code implementation • CVPR 2021 • Angela Dai, Yawar Siddiqui, Justus Thies, Julien Valentin, Matthias Nießner
We present SPSG, a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations by learning to infer unobserved scene geometry and color in a self-supervised fashion.
1 code implementation • CVPR 2020 • Yawar Siddiqui, Julien Valentin, Matthias Nießner
To incorporate this uncertainty measure, we introduce a new viewpoint entropy formulation, which is the basis of our active learning strategy.
1 code implementation • NeurIPS 2019 • Srinath Sridhar, Davis Rempe, Julien Valentin, Sofien Bouaziz, Leonidas J. Guibas
We investigate the problem of learning category-specific 3D shape reconstruction from a variable number of RGB views of previously unobserved object instances.
9 code implementations • CVPR 2019 • He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #2 on
6D Pose Estimation using RGBD
on CAMERA25
no code implementations • 12 Nov 2018 • Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
1 code implementation • 29 Oct 2018 • Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Victor A. Prisacariu, Luigi Di Stefano, Philip H. S. Torr
The adapted forests achieved relocalisation performance that was on par with that of offline forests, and our approach was able to estimate the camera pose in close to real time.
2 code implementations • ECCV 2018 • Sameh Khamis, Sean Fanello, Christoph Rhemann, Adarsh Kowdle, Julien Valentin, Shahram Izadi
A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.
Ranked #2 on
Stereo Depth Estimation
on sceneflow
1 code implementation • ECCV 2018 • Yinda Zhang, Sameh Khamis, Christoph Rhemann, Julien Valentin, Adarsh Kowdle, Vladimir Tankovich, Michael Schoenberg, Shahram Izadi, Thomas Funkhouser, Sean Fanello
In this paper we present ActiveStereoNet, the first deep learning solution for active stereo systems.
no code implementations • 28 Oct 2017 • Lili Meng, Frederick Tung, James J. Little, Julien Valentin, Clarence de Silva
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure and loop closure detection.
1 code implementation • 22 Oct 2017 • Lili Meng, Jianhui Chen, Frederick Tung, James J. Little, Julien Valentin, Clarence W. de Silva
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
no code implementations • ICCV 2017 • Sean Ryan Fanello, Julien Valentin, Adarsh Kowdle, Christoph Rhemann, Vladimir Tankovich, Carlo Ciliberto, Philip Davidson, Shahram Izadi
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
no code implementations • CVPR 2017 • Sean Ryan Fanello, Julien Valentin, Christoph Rhemann, Adarsh Kowdle, Vladimir Tankovich, Philip Davidson, Shahram Izadi
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
no code implementations • CVPR 2017 • Tommaso Cavallari, Stuart Golodetz, Nicholas A. Lord, Julien Valentin, Luigi Di Stefano, Philip H. S. Torr
Camera relocalisation is an important problem in computer vision, with applications in simultaneous localisation and mapping, virtual/augmented reality and navigation.
no code implementations • 18 Mar 2016 • Julien Valentin, Angela Dai, Matthias Nießner, Pushmeet Kohli, Philip Torr, Shahram Izadi, Cem Keskin
We demonstrate the efficacy of our approach on the challenging problem of RGB Camera Relocalization.
no code implementations • 10 Jan 2016 • Anurag Arnab, Michael Sapienza, Stuart Golodetz, Julien Valentin, Ondrej Miksik, Shahram Izadi, Philip Torr
It is not always possible to recognise objects and infer material properties for a scene from visual cues alone, since objects can look visually similar whilst being made of very different materials.
no code implementations • CVPR 2015 • Julien Valentin, Matthias Niessner, Jamie Shotton, Andrew Fitzgibbon, Shahram Izadi, Philip H. S. Torr
Recent advances in camera relocalization use predictions from a regression forest to guide the camera pose optimization procedure.
no code implementations • 3 Oct 2014 • Victor Adrian Prisacariu, Olaf Kähler, Ming Ming Cheng, Carl Yuheng Ren, Julien Valentin, Philip H. S. Torr, Ian D. Reid, David W. Murray
Along with the framework we also provide a set of components for scalable reconstruction: two implementations of camera trackers, based on RGB data and on depth data, two representations of the 3D volumetric data, a dense volume and one based on hashes of subblocks, and an optional module for swapping subblocks in and out of the typically limited GPU memory.