Search Results for author: Julien Valentin

Found 25 papers, 11 papers with code

BlendFields: Few-Shot Example-Driven Facial Modeling

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.

Dynamic Point Fields

no code implementations 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.

Surface Reconstruction

X-Avatar: Expressive Human Avatars

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.

3D Human Reconstruction

VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations

no code implementations1 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.

Novel View Synthesis

Learning to Fit Morphable Models

no code implementations29 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.

FastNeRF: High-Fidelity Neural Rendering at 200FPS

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.

Mixed Reality Neural Rendering +1

SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans

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.

3D Reconstruction Scene Generation

ViewAL: Active Learning with Viewpoint Entropy for Semantic Segmentation

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.

Active Learning Semantic Segmentation +1

Multiview Aggregation for Learning Category-Specific Shape Reconstruction

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.

3D Shape Reconstruction

LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

no code implementations12 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.

Denoising Super-Resolution

Real-Time RGB-D Camera Pose Estimation in Novel Scenes using a Relocalisation Cascade

1 code implementation29 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.

Pose Estimation

StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

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.

Depth Prediction Quantization +3

Exploiting Points and Lines in Regression Forests for RGB-D Camera Relocalization

no code implementations28 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.

Camera Relocalization Loop Closure Detection +1

Backtracking Regression Forests for Accurate Camera Relocalization

1 code implementation22 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.

Camera Relocalization Loop Closure Detection +2

Low Compute and Fully Parallel Computer Vision With HashMatch

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.

Disparity Estimation Image Retrieval +2

On-the-Fly Adaptation of Regression Forests for Online Camera Relocalisation

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.

Camera Relocalization regression

Joint Object-Material Category Segmentation from Audio-Visual Cues

no code implementations10 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.

A Framework for the Volumetric Integration of Depth Images

no code implementations3 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.

3D Reconstruction

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