Get our free extension to see links to code for papers anywhere online!

Chrome logo  Add to Chrome

Firefox logo Add to Firefox
Picture for Oshri Halimi

Oshri Halimi
Alert button

PhysGraph: Physics-Based Integration Using Graph Neural Networks


Jan 27, 2023
Oshri Halimi, Egor Larionov, Zohar Barzelay, Philipp Herholz, Tuur Stuyck

Add code
Alert button

Figure 1 for PhysGraph: Physics-Based Integration Using Graph Neural Networks
Figure 2 for PhysGraph: Physics-Based Integration Using Graph Neural Networks
Figure 3 for PhysGraph: Physics-Based Integration Using Graph Neural Networks
Figure 4 for PhysGraph: Physics-Based Integration Using Graph Neural Networks

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

FETA: Towards Specializing Foundation Models for Expert Task Applications


Sep 08, 2022
Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Kate Saenko, PeterW. J. Staar, Rogerio Feris, Leonid Karlinsky

Add code
Alert button

Figure 1 for FETA: Towards Specializing Foundation Models for Expert Task Applications
Figure 2 for FETA: Towards Specializing Foundation Models for Expert Task Applications
Figure 3 for FETA: Towards Specializing Foundation Models for Expert Task Applications
Figure 4 for FETA: Towards Specializing Foundation Models for Expert Task Applications

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Garment Avatars: Realistic Cloth Driving using Pattern Registration


Jun 07, 2022
Oshri Halimi, Fabian Prada, Tuur Stuyck, Donglai Xiang, Timur Bagautdinov, He Wen, Ron Kimmel, Takaaki Shiratori, Chenglei Wu, Yaser Sheikh

Add code
Alert button

Figure 1 for Garment Avatars: Realistic Cloth Driving using Pattern Registration
Figure 2 for Garment Avatars: Realistic Cloth Driving using Pattern Registration
Figure 3 for Garment Avatars: Realistic Cloth Driving using Pattern Registration
Figure 4 for Garment Avatars: Realistic Cloth Driving using Pattern Registration

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

LIMP: Learning Latent Shape Representations with Metric Preservation Priors


Mar 27, 2020
Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà

Add code
Alert button

Figure 1 for LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Figure 2 for LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Figure 3 for LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Figure 4 for LIMP: Learning Latent Shape Representations with Metric Preservation Priors

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

The Whole Is Greater Than the Sum of Its Nonrigid Parts


Jan 27, 2020
Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel

Add code
Alert button

Figure 1 for The Whole Is Greater Than the Sum of Its Nonrigid Parts
Figure 2 for The Whole Is Greater Than the Sum of Its Nonrigid Parts
Figure 3 for The Whole Is Greater Than the Sum of Its Nonrigid Parts
Figure 4 for The Whole Is Greater Than the Sum of Its Nonrigid Parts

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email

Self-supervised Learning of Dense Shape Correspondence


Dec 06, 2018
Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel

Add code
Alert button

Figure 1 for Self-supervised Learning of Dense Shape Correspondence
Figure 2 for Self-supervised Learning of Dense Shape Correspondence
Figure 3 for Self-supervised Learning of Dense Shape Correspondence
Figure 4 for Self-supervised Learning of Dense Shape Correspondence

   Access Paper or Ask Questions
  • Share via Twitter
  • Share via Facebook
  • Share via LinkedIn
  • Share via Whatsapp
  • Share via Messenger
  • Share via Email