Search Results for author: Ricardo Martin-Brualla

Found 17 papers, 4 papers with code

HyperNeRF: A Higher-Dimensional Representation for Topologically Varying Neural Radiance Fields

1 code implementation24 Jun 2021 Keunhong Park, Utkarsh Sinha, Peter Hedman, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Ricardo Martin-Brualla, Steven M. Seitz

A common approach to reconstruct such non-rigid scenes is through the use of a learned deformation field mapping from coordinates in each input image into a canonical template coordinate space.

Novel View Synthesis

FiG-NeRF: Figure-Ground Neural Radiance Fields for 3D Object Category Modelling

no code implementations17 Apr 2021 Christopher Xie, Keunhong Park, Ricardo Martin-Brualla, Matthew Brown

We investigate the use of Neural Radiance Fields (NeRF) to learn high quality 3D object category models from collections of input images.

Neural RGB-D Surface Reconstruction

no code implementations9 Apr 2021 Dejan Azinović, Ricardo Martin-Brualla, Dan B Goldman, Matthias Nießner, Justus Thies

In contrast to a density field as the underlying geometry representation, we propose to learn a deep neural network which stores a truncated signed distance field.

Image Generation Novel View Synthesis

Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields

1 code implementation24 Mar 2021 Jonathan T. Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin-Brualla, Pratul P. Srinivasan

Mip-NeRF is also able to match the accuracy of a brute-force supersampled NeRF on our multiscale dataset while being 22x faster.

IBRNet: Learning Multi-View Image-Based Rendering

no code implementations CVPR 2021 Qianqian Wang, Zhicheng Wang, Kyle Genova, Pratul Srinivasan, Howard Zhou, Jonathan T. Barron, Ricardo Martin-Brualla, Noah Snavely, Thomas Funkhouser

Unlike neural scene representation work that optimizes per-scene functions for rendering, we learn a generic view interpolation function that generalizes to novel scenes.

Neural Rendering Novel View Synthesis

ShaRF: Shape-conditioned Radiance Fields from a Single View

no code implementations17 Feb 2021 Konstantinos Rematas, Ricardo Martin-Brualla, Vittorio Ferrari

We demonstrate in several experiments the effectiveness of our approach in both synthetic and real images.

Time-Travel Rephotography

no code implementations22 Dec 2020 Xuan Luo, Xuaner Zhang, Paul Yoo, Ricardo Martin-Brualla, Jason Lawrence, Steven M. Seitz

Many historical people are captured only in old, faded, black and white photos, that have been distorted by the limitations of early cameras and the passage of time.

Colorization Denoising +1

Nerfies: Deformable Neural Radiance Fields

1 code implementation25 Nov 2020 Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B Goldman, Steven M. Seitz, Ricardo Martin-Brualla

We present the first method capable of photorealistically reconstructing deformable scenes using photos/videos captured casually from mobile phones.

3D Human Reconstruction

GeLaTO: Generative Latent Textured Objects

no code implementations ECCV 2020 Ricardo Martin-Brualla, Rohit Pandey, Sofien Bouaziz, Matthew Brown, Dan B. Goldman

Accurate modeling of 3D objects exhibiting transparency, reflections and thin structures is an extremely challenging problem.

NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections

1 code implementation CVPR 2021 Ricardo Martin-Brualla, Noha Radwan, Mehdi S. M. Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, Daniel Duckworth

We present a learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs.

State of the Art on Neural Rendering

no code implementations8 Apr 2020 Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer

Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.

Image Generation Neural Rendering +1

KeystoneDepth: Visualizing History in 3D

no code implementations21 Aug 2019 Xuan Luo, Yanmeng Kong, Jason Lawrence, Ricardo Martin-Brualla, Steve Seitz

This paper introduces the largest and most diverse collection of rectified stereo image pairs to the research community, KeystoneDepth, consisting of tens of thousands of stereographs of historical people, events, objects, and scenes between 1860 and 1963.

Rectification

Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning

no code implementations CVPR 2019 Rohit Pandey, Anastasia Tkach, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Ricardo Martin-Brualla, Andrea Tagliasacchi, George Papandreou, Philip Davidson, Cem Keskin, Shahram Izadi, Sean Fanello

The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.

Neural Rerendering in the Wild

no code implementations CVPR 2019 Moustafa Meshry, Dan B. Goldman, Sameh Khamis, Hugues Hoppe, Rohit Pandey, Noah Snavely, Ricardo Martin-Brualla

Starting from internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos and approximate the scene as a point cloud.

3D 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 +1

3D Time-lapse Reconstruction from Internet Photos

no code implementations ICCV 2015 Ricardo Martin-Brualla, David Gallup, Steven M. Seitz

Given an Internet photo collection of a landmark, we compute a 3D time-lapse video sequence where a virtual camera moves continuously in time and space.

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