Search Results for author: Christian Richardt

Found 23 papers, 6 papers with code

360° Optical Flow using Tangent Images

1 code implementation28 Dec 2021 Mingze Yuan, Christian Richardt

Our method leverages gnomonic projection to locally convert ERP images to perspective images, and uniformly samples the ERP image by projection to a cubemap and regular icosahedron vertices, to incrementally refine the estimated 360{\deg} flow fields even in the presence of large rotations.

Optical Flow Estimation

360MonoDepth: High-Resolution 360° Monocular Depth Estimation

no code implementations30 Nov 2021 Manuel Rey-Area, Mingze Yuan, Christian Richardt

360{\deg} cameras can capture complete environments in a single shot, which makes 360{\deg} imagery alluring in many computer vision tasks.

Monocular Depth Estimation Novel View Synthesis

MatryODShka: Real-time 6DoF Video View Synthesis using Multi-Sphere Images

1 code implementation ECCV 2020 Benjamin Attal, Selena Ling, Aaron Gokaslan, Christian Richardt, James Tompkin

Our approach is to simultaneously learn depth and disocclusions via a multi-sphere image representation, which can be rendered with correct 6DoF disparity and motion parallax in VR.

Combining Task Predictors via Enhancing Joint Predictability

no code implementations ECCV 2020 Kwang In Kim, Christian Richardt, Hyung Jin Chang

Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the internals of individual predictors.

Multi-class Classification

BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images

1 code implementation NeurIPS 2020 Thu Nguyen-Phuoc, Christian Richardt, Long Mai, Yong-Liang Yang, Niloy Mitra

Our experiments show that using explicit 3D features to represent objects allows BlockGAN to learn disentangled representations both in terms of objects (foreground and background) and their properties (pose and identity).

Representation Learning

Neural Style-Preserving Visual Dubbing

no code implementations5 Sep 2019 Hyeongwoo Kim, Mohamed Elgharib, Michael Zollhöfer, Hans-Peter Seidel, Thabo Beeler, Christian Richardt, Christian Theobalt

We present a style-preserving visual dubbing approach from single video inputs, which maintains the signature style of target actors when modifying facial expressions, including mouth motions, to match foreign languages.

Real-Time Global Illumination Decomposition of Videos

no code implementations6 Aug 2019 Abhimitra Meka, Mohammad Shafiei, Michael Zollhoefer, Christian Richardt, Christian Theobalt

We propose the first approach for the decomposition of a monocular color video into direct and indirect illumination components in real time.

Frame

HoloGAN: Unsupervised learning of 3D representations from natural images

2 code implementations ICCV 2019 Thu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt, Yong-Liang Yang

This shows that HoloGAN is the first generative model that learns 3D representations from natural images in an entirely unsupervised manner.

Image Generation Novel View Synthesis

Unsupervised Attention-guided Image-to-Image Translation

1 code implementation NeurIPS 2018 Youssef Alami Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.

Translation Unsupervised Image-To-Image Translation

Unsupervised Attention-guided Image to Image Translation

2 code implementations6 Jun 2018 Youssef A. Mejjati, Christian Richardt, James Tompkin, Darren Cosker, Kwang In Kim

Current unsupervised image-to-image translation techniques struggle to focus their attention on individual objects without altering the background or the way multiple objects interact within a scene.

Translation Unsupervised Image-To-Image Translation

Deep Video Portraits

no code implementations29 May 2018 Hyeongwoo Kim, Pablo Garrido, Ayush Tewari, Weipeng Xu, Justus Thies, Matthias Nießner, Patrick Pérez, Christian Richardt, Michael Zollhöfer, Christian Theobalt

In order to enable source-to-target video re-animation, we render a synthetic target video with the reconstructed head animation parameters from a source video, and feed it into the trained network -- thus taking full control of the target.

Face Model

LIME: Live Intrinsic Material Estimation

no code implementations CVPR 2018 Abhimitra Meka, Maxim Maximov, Michael Zollhoefer, Avishek Chatterjee, Hans-Peter Seidel, Christian Richardt, Christian Theobalt

We present the first end to end approach for real time material estimation for general object shapes with uniform material that only requires a single color image as input.

Frame Image-to-Image Translation +2

Predictor Combination at Test Time

no code implementations ICCV 2017 Kwang In Kim, James Tompkin, Christian Richardt

We present an algorithm for test-time combination of a set of reference predictors with unknown parametric forms.

Denoising Transfer Learning

InverseFaceNet: Deep Monocular Inverse Face Rendering

no code implementations CVPR 2018 Hyeongwoo Kim, Michael Zollhöfer, Ayush Tewari, Justus Thies, Christian Richardt, Christian Theobalt

In contrast, we propose to recover high-quality facial pose, shape, expression, reflectance and illumination using a deep neural network that is trained using a large, synthetically created training corpus.

Face Reconstruction

EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras (Extended Abstract)

no code implementations31 Dec 2016 Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt

Marker-based and marker-less optical skeletal motion-capture methods use an outside-in arrangement of cameras placed around a scene, with viewpoints converging on the center.

Pose Estimation

Real-time Halfway Domain Reconstruction of Motion and Geometry

no code implementations23 Oct 2016 Lucas Thies, Michael Zollhöfer, Christian Richardt, Christian Theobalt, Günther Greiner

Our extensive experiments and evaluations show that our approach produces high-quality dense reconstructions of 3D geometry and scene flow at real-time frame rates, and compares favorably to the state of the art.

Frame

Video Depth-From-Defocus

no code implementations12 Oct 2016 Hyeongwoo Kim, Christian Richardt, Christian Theobalt

Many compelling video post-processing effects, in particular aesthetic focus editing and refocusing effects, are feasible if per-frame depth information is available.

Frame

EgoCap: Egocentric Marker-less Motion Capture with Two Fisheye Cameras

no code implementations23 Sep 2016 Helge Rhodin, Christian Richardt, Dan Casas, Eldar Insafutdinov, Mohammad Shafiei, Hans-Peter Seidel, Bernt Schiele, Christian Theobalt

We therefore propose a new method for real-time, marker-less and egocentric motion capture which estimates the full-body skeleton pose from a lightweight stereo pair of fisheye cameras that are attached to a helmet or virtual reality headset.

Pose Estimation

Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras

no code implementations16 Sep 2016 Christian Richardt, Hyeongwoo Kim, Levi Valgaerts, Christian Theobalt

We finally refine the computed correspondence fields in a variational scene flow formulation.

A Versatile Scene Model with Differentiable Visibility Applied to Generative Pose Estimation

no code implementations ICCV 2015 Helge Rhodin, Nadia Robertini, Christian Richardt, Hans-Peter Seidel, Christian Theobalt

Generative reconstruction methods compute the 3D configuration (such as pose and/or geometry) of a shape by optimizing the overlap of the projected 3D shape model with images.

Occlusion Handling Pose Estimation

Megastereo: Constructing High-Resolution Stereo Panoramas

no code implementations CVPR 2013 Christian Richardt, Yael Pritch, Henning Zimmer, Alexander Sorkine-Hornung

As our first contribution, we describe the necessary correction steps and a compact representation for the input images in order to achieve a highly accurate approximation to the required ray space.

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