Search Results for author: Ira Kemelmacher-Shlizerman

Found 28 papers, 10 papers with code

HRTF Estimation in the Wild

no code implementations6 Nov 2023 Vivek Jayaram, Ira Kemelmacher-Shlizerman, Steven M. Seitz

Our approach offers an efficient and accessible method for deriving personalized HRTFs and has the potential to greatly improve spatial audio experiences.

Animating Street View

no code implementations12 Oct 2023 Mengyi Shan, Brian Curless, Ira Kemelmacher-Shlizerman, Steve Seitz

We present a system that automatically brings street view imagery to life by populating it with naturally behaving, animated pedestrians and vehicles.

Total Selfie: Generating Full-Body Selfies

no code implementations28 Aug 2023 Bowei Chen, Brian Curless, Ira Kemelmacher-Shlizerman, Steven M. Seitz

We present a method to generate full-body selfies from photographs originally taken at arms length.

TryOnDiffusion: A Tale of Two UNets

1 code implementation CVPR 2023 Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, Ira Kemelmacher-Shlizerman

Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person.

Virtual Try-on

PersonNeRF: Personalized Reconstruction from Photo Collections

no code implementations CVPR 2023 Chung-Yi Weng, Pratul P. Srinivasan, Brian Curless, Ira Kemelmacher-Shlizerman

We present PersonNeRF, a method that takes a collection of photos of a subject (e. g. Roger Federer) captured across multiple years with arbitrary body poses and appearances, and enables rendering the subject with arbitrary novel combinations of viewpoint, body pose, and appearance.

HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video

1 code implementation CVPR 2022 Chung-Yi Weng, Brian Curless, Pratul P. Srinivasan, Jonathan T. Barron, Ira Kemelmacher-Shlizerman

Our method optimizes for a volumetric representation of the person in a canonical T-pose, in concert with a motion field that maps the estimated canonical representation to every frame of the video via backward warps.

A Light Stage on Every Desk

no code implementations ICCV 2021 Soumyadip Sengupta, Brian Curless, Ira Kemelmacher-Shlizerman, Steve Seitz

Whereas existing light stages require expensive, room-scale spherical capture gantries and exist in only a few labs in the world, we demonstrate how to acquire useful data from a normal TV or desktop monitor.

TryOnGAN: Body-Aware Try-On via Layered Interpolation

4 code implementations6 Jan 2021 Kathleen M Lewis, Srivatsan Varadharajan, Ira Kemelmacher-Shlizerman

Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person.

Vid2Actor: Free-viewpoint Animatable Person Synthesis from Video in the Wild

no code implementations23 Dec 2020 Chung-Yi Weng, Brian Curless, Ira Kemelmacher-Shlizerman

At the core of our method is a volumetric 3D human representation reconstructed with a deep network trained on input video, enabling novel pose/view synthesis.

Image-to-Image Translation Translation

Real-Time High-Resolution Background Matting

2 code implementations CVPR 2021 Shanchuan Lin, Andrey Ryabtsev, Soumyadip Sengupta, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman

We introduce a real-time, high-resolution background replacement technique which operates at 30fps in 4K resolution, and 60fps for HD on a modern GPU.

4k Image Matting +1

The Cone of Silence: Speech Separation by Localization

1 code implementation NeurIPS 2020 Teerapat Jenrungrot, Vivek Jayaram, Steve Seitz, Ira Kemelmacher-Shlizerman

Given a multi-microphone recording of an unknown number of speakers talking concurrently, we simultaneously localize the sources and separate the individual speakers.

Audio Source Separation Speech Separation

Reconstructing NBA Players

2 code implementations ECCV 2020 Luyang Zhu, Konstantinos Rematas, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman

Based on these models, we introduce a new method that takes as input a single photo of a clothed player in any basketball pose and outputs a high resolution mesh and 3D pose for that player.

Background Matting: The World is Your Green Screen

1 code implementation CVPR 2020 Soumyadip Sengupta, Vivek Jayaram, Brian Curless, Steve Seitz, Ira Kemelmacher-Shlizerman

To bridge the domain gap to real imagery with no labeling, we train another matting network guided by the first network and by a discriminator that judges the quality of composites.

Image Matting

Lifespan Age Transformation Synthesis

2 code implementations ECCV 2020 Roy Or-El, Soumyadip Sengupta, Ohad Fried, Eli Shechtman, Ira Kemelmacher-Shlizerman

Most existing aging methods are limited to changing the texture, overlooking transformations in head shape that occur during the human aging and growth process.

Face Age Editing Generative Adversarial Network +5

Photo Wake-Up: 3D Character Animation from a Single Photo

no code implementations CVPR 2019 Chung-Yi Weng, Brian Curless, Ira Kemelmacher-Shlizerman

The key contributions of this paper are: 1) an application of viewing and animating humans in single photos in 3D, 2) a novel 2D warping method to deform a posable template body model to fit the person's complex silhouette to create an animatable mesh, and 3) a method for handling partial self occlusions.

3D Character Animation From A Single Photo

Head Reconstruction from Internet Photos

no code implementations13 Sep 2018 Shu Liang, Linda G. Shapiro, Ira Kemelmacher-Shlizerman

Our method is to gradually "grow" the head mesh starting from the frontal face and extending to the rest of views using photometric stereo constraints.

3D Face Reconstruction

3D Face Hallucination from a Single Depth Frame

no code implementations13 Sep 2018 Shu Liang, Ira Kemelmacher-Shlizerman, Linda G. Shapiro

We further combine the input depth frame with the matched database shapes into a single mesh that results in a high-resolution shape of the input person.

Face Hallucination Hallucination

Video to Fully Automatic 3D Hair Model

no code implementations13 Sep 2018 Shu Liang, Xiufeng Huang, Xianyu Meng, Kunyao Chen, Linda G. Shapiro, Ira Kemelmacher-Shlizerman

In this paper, we describe a system that can completely automatically create a reconstruction from any video (even a selfie video), and we don't require specific views, since taking your -90 degree, 90 degree, and full back views is not feasible in a selfie capture.

Soccer on Your Tabletop

no code implementations CVPR 2018 Konstantinos Rematas, Ira Kemelmacher-Shlizerman, Brian Curless, Steve Seitz

We present a system that transforms a monocular video of a soccer game into a moving 3D reconstruction, in which the players and field can be rendered interactively with a 3D viewer or through an Augmented Reality device.

3D Reconstruction Depth Estimation

Audio to Body Dynamics

1 code implementation CVPR 2018 Eli Shlizerman, Lucio M. Dery, Hayden Schoen, Ira Kemelmacher-Shlizerman

We present a method that gets as input an audio of violin or piano playing, and outputs a video of skeleton predictions which are further used to animate an avatar.

Level Playing Field for Million Scale Face Recognition

no code implementations CVPR 2017 Aaron Nech, Ira Kemelmacher-Shlizerman

Some key discoveries: 1) algorithms, trained on MF2, were able to achieve state of the art and comparable results to algorithms trained on massive private sets, 2) some outperformed themselves once trained on MF2, 3) invariance to aging suffers from low accuracies as in MegaFace, identifying the need for larger age variations possibly within identities or adjustment of algorithms in future testings.

Face Recognition

The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

no code implementations CVPR 2016 Ira Kemelmacher-Shlizerman, Steve Seitz, Daniel Miller, Evan Brossard

Our key observations are that testing at the million scale reveals big performance differences (of algorithms that perform similarly well on smaller scale) and that age invariant recognition as well as pose are still challenging for most.

Face Recognition

What Makes Tom Hanks Look Like Tom Hanks

no code implementations ICCV 2015 Supasorn Suwajanakorn, Steven M. Seitz, Ira Kemelmacher-Shlizerman

We reconstruct a controllable model of a person from a large photo collection that captures his or her persona, i. e., physical appearance and behavior.

3D Face Reconstruction

What Makes Kevin Spacey Look Like Kevin Spacey

no code implementations2 Jun 2015 Supasorn Suwajanakorn, Ira Kemelmacher-Shlizerman, Steve Seitz

We reconstruct a controllable model of a person from a large photo collection that captures his or her {\em persona}, i. e., physical appearance and behavior.

3D Face Reconstruction

Illumination-Aware Age Progression

no code implementations CVPR 2014 Ira Kemelmacher-Shlizerman, Supasorn Suwajanakorn, Steven M. Seitz

We present an approach that takes a single photograph of a child as input and automatically produces a series of age-progressed outputs between 1 and 80 years of age, accounting for pose, expression, and illumination.

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