Search Results for author: Shahram Izadi

Found 31 papers, 4 papers with code

RePose: Learning Deep Kinematic Priors for Fast Human Pose Estimation

no code implementations10 Feb 2020 Hossam Isack, Christian Haene, Cem Keskin, Sofien Bouaziz, Yuri Boykov, Shahram Izadi, Sameh Khamis

At the coarsest resolution, and in a manner similar to classical part-based approaches, we leverage the kinematic structure of the human body to propagate convolutional feature updates between the keypoints or body parts.

Pose Estimation

MIST: Multiple Instance Spatial Transformer Networks

no code implementations25 Sep 2019 Baptiste Angles, Simon Kornblith, Shahram Izadi, Andrea Tagliasacchi, Kwang Moo Yi

We propose a deep network that can be trained to tackle image reconstruction and classification problems that involve detection of multiple object instances, without any supervision regarding their whereabouts.

Image Reconstruction

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.

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

SplineNets: Continuous Neural Decision Graphs

no code implementations NeurIPS 2018 Cem Keskin, Shahram Izadi

We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs).

Position

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

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.

Computational Efficiency Disparity Estimation +3

Fits Like a Glove: Rapid and Reliable Hand Shape Personalization

no code implementations CVPR 2016 David Joseph Tan, Thomas Cashman, Jonathan Taylor, Andrew Fitzgibbon, Daniel Tarlow, Sameh Khamis, Shahram Izadi, Jamie Shotton

We present a fast, practical method for personalizing a hand shape basis to an individual user's detailed hand shape using only a small set of depth images.

The Global Patch Collider

no code implementations CVPR 2016 Shenlong Wang, Sean Ryan Fanello, Christoph Rhemann, Shahram Izadi, Pushmeet Kohli

In contrast to conventional approaches that rely on pairwise distance computation, our algorithm isolates distinctive pixel pairs that hit the same leaf during traversal through multiple learned tree structures.

Optical Flow Estimation Stereo Matching +1

BundleFusion: Real-time Globally Consistent 3D Reconstruction using On-the-fly Surface Re-integration

1 code implementation5 Apr 2016 Angela Dai, Matthias Nießner, Michael Zollhöfer, Shahram Izadi, Christian Theobalt

Our approach estimates globally optimized (i. e., bundle adjusted) poses in real-time, supports robust tracking with recovery from gross tracking failures (i. e., relocalization), and re-estimates the 3D model in real-time to ensure global consistency; all within a single framework.

3D Reconstruction Mixed Reality +1

DeepContext: Context-Encoding Neural Pathways for 3D Holistic Scene Understanding

no code implementations ICCV 2017 Yinda Zhang, Mingru Bai, Pushmeet Kohli, Shahram Izadi, Jianxiong Xiao

In particular, 3D context has been shown to be an extremely important cue for scene understanding - yet very little research has been done on integrating context information with deep models.

Object Scene Understanding

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.

Object

Learning an Efficient Model of Hand Shape Variation From Depth Images

no code implementations CVPR 2015 Sameh Khamis, Jonathan Taylor, Jamie Shotton, Cem Keskin, Shahram Izadi, Andrew Fitzgibbon

We represent the observed surface using Loop subdivision of a control mesh that is deformed by our learned parametric shape and pose model.

A Light Transport Model for Mitigating Multipath Interference in Time-of-Flight Sensors

no code implementations CVPR 2015 Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

Computationally Bounded Retrieval

no code implementations CVPR 2015 Mohammad Rastegari, Cem Keskin, Pushmeet Kohli, Shahram Izadi

We demonstrate this technique on large retrieval databases, specifically ImageNET, GIST1M and SUN-attribute for the task of nearest neighbor retrieval, and show that our method achieves a speed-up of up to a factor of 100 over state-of-the-art methods, while having on-par and in some cases even better accuracy.

Attribute Image Retrieval +1

A Light Transport Model for Mitigating Multipath Interference in TOF Sensors

no code implementations CVPR 2015 Nikhil Naik, Achuta Kadambi, Christoph Rhemann, Shahram Izadi, Ramesh Raskar, Sing Bing Kang

Continuous-wave Time-of-flight (TOF) range imaging has become a commercially viable technology with many applications in computer vision and graphics.

Multi-Output Learning for Camera Relocalization

no code implementations CVPR 2014 Abner Guzman-Rivera, Pushmeet Kohli, Ben Glocker, Jamie Shotton, Toby Sharp, Andrew Fitzgibbon, Shahram Izadi

We formulate this problem as inversion of the generative rendering procedure, i. e., we want to find the camera pose corresponding to a rendering of the 3D scene model that is most similar with the observed input.

3D Reconstruction Camera Relocalization

Filter Forests for Learning Data-Dependent Convolutional Kernels

no code implementations CVPR 2014 Sean Ryan Fanello, Cem Keskin, Pushmeet Kohli, Shahram Izadi, Jamie Shotton, Antonio Criminisi, Ugo Pattacini, Tim Paek

We propose 'filter forests' (FF), an efficient new discriminative approach for predicting continuous variables given a signal and its context.

Denoising

KinectFusion: Real-Time Dense Surface Mapping and Tracking

no code implementations ISMAR 2011 Richard A. Newcombe, Shahram Izadi, Otmar Hilliges, David Molyneaux, David Kim, Andrew J. Davison, Pushmeet Kohli, Jamie Shotton, Steve Hodges, Andrew Fitzgibbon

We present a system for accurate real-time mapping of complex and arbitrary indoor scenes in variable lighting conditions, using only a moving low-cost depth camera and commodity graphics hardware.

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