no code implementations • 27 Aug 2024 • Hyunwoo Kim, Itai Lang, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, Rana Hanocka
We propose MeshUp, a technique that deforms a 3D mesh towards multiple target concepts, and intuitively controls the region where each concept is expressed.
no code implementations • 11 Jun 2024 • Xin Yuan, Rana Hanocka, Michael Maire
From a collection of unannotated 2D images of a scene, our approach simultaneously learns both a network to predict camera pose from 2D image input, as well as the parameters of a Neural Radiance Field (NeRF) for the 3D scene.
no code implementations • 4 Apr 2024 • Itai Lang, Fei Xu, Dale Decatur, Sudarshan Babu, Rana Hanocka
We present iSeg, a new interactive technique for segmenting 3D shapes.
no code implementations • 14 Feb 2024 • Chen Dudai, Morris Alper, Hana Bezalel, Rana Hanocka, Itai Lang, Hadar Averbuch-Elor
To bolster such models with fine-grained knowledge, we leverage large-scale Internet data containing images of similar landmarks along with weakly-related textual information.
no code implementations • 5 Dec 2023 • Boheng Zhao, Rana Hanocka, Raymond A. Yeh
Ambigrams are calligraphic designs that have different meanings depending on the viewing orientation.
1 code implementation • CVPR 2024 • Dale Decatur, Itai Lang, Kfir Aberman, Rana Hanocka
In this work we develop 3D Paintbrush, a technique for automatically texturing local semantic regions on meshes via text descriptions.
no code implementations • 26 Oct 2023 • Sudarshan Babu, Richard Liu, Avery Zhou, Michael Maire, Greg Shakhnarovich, Rana Hanocka
We introduce HyperFields, a method for generating text-conditioned Neural Radiance Fields (NeRFs) with a single forward pass and (optionally) some fine-tuning.
no code implementations • 27 Jul 2023 • Zihan Zhang, Richard Liu, Kfir Aberman, Rana Hanocka
The gradual nature of a diffusion process that synthesizes samples in small increments constitutes a key ingredient of Denoising Diffusion Probabilistic Models (DDPM), which have presented unprecedented quality in image synthesis and been recently explored in the motion domain.
1 code implementation • 26 Apr 2023 • William Gao, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, Rana Hanocka
Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients.
1 code implementation • CVPR 2023 • Dale Decatur, Itai Lang, Rana Hanocka
A key feature of our system is the ability to interpret "out-of-domain" localizations.
1 code implementation • CVPR 2023 • Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka
We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization.
no code implementations • 9 Dec 2022 • Nam Anh Dinh, Haochen Wang, Greg Shakhnarovich, Rana Hanocka
There is no settled universal 3D representation for geometry with many alternatives such as point clouds, meshes, implicit functions, and voxels to name a few.
no code implementations • 11 Oct 2022 • William Gao, April Wang, Gal Metzer, Raymond A. Yeh, Rana Hanocka
We present TetGAN, a convolutional neural network designed to generate tetrahedral meshes.
1 code implementation • 5 May 2022 • Peizhuo Li, Kfir Aberman, Zihan Zhang, Rana Hanocka, Olga Sorkine-Hornung
We present GANimator, a generative model that learns to synthesize novel motions from a single, short motion sequence.
1 code implementation • 5 Jan 2022 • Meitar Shechter, Rana Hanocka, Gal Metzer, Raja Giryes, Daniel Cohen-Or
In this work, we opt to learn the weighting function, by training a neural network on the control points from a single input shape, and exploit the innate smoothness of neural networks.
1 code implementation • CVPR 2022 • Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, Rana Hanocka
In order to modify style, we obtain a similarity score between a text prompt (describing style) and a stylized mesh by harnessing the representational power of CLIP.
Ranked #1 on
Neural Stylization
on Meshes
1 code implementation • CVPR 2022 • R. Kenny Jones, Aalia Habib, Rana Hanocka, Daniel Ritchie
We propose the Neurally-Guided Shape Parser (NGSP), a method that learns how to assign fine-grained semantic labels to regions of a 3D shape.
1 code implementation • 30 May 2021 • Gal Metzer, Rana Hanocka, Raja Giryes, Niloy J. Mitra, Daniel Cohen-Or
We present a technique for visualizing point clouds using a neural network.
1 code implementation • 6 May 2021 • Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, Baoquan Chen
Furthermore, we propose neural blend shapes--a set of corrective pose-dependent shapes which improve the deformation quality in the joint regions in order to address the notorious artifacts resulting from standard rigging and skinning.
1 code implementation • 4 May 2021 • Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, Daniel Cohen-Or
In the global phase, we propagate the orientation across all coherent patches using a dipole propagation.
1 code implementation • 14 Aug 2020 • Gal Metzer, Rana Hanocka, Raja Giryes, Daniel Cohen-Or
We introduce a novel technique for neural point cloud consolidation which learns from only the input point cloud.
1 code implementation • 30 Jun 2020 • Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or
Learning and synthesizing on local geometric patches enables a genus-oblivious framework, facilitating texture transfer between shapes of different genus.
2 code implementations • 22 May 2020 • Rana Hanocka, Gal Metzer, Raja Giryes, Daniel Cohen-Or
We optimize the network weights to deform an initial mesh to shrink-wrap a single input point cloud.
1 code implementation • CVPR 2020 • Amir Hertz, Rana Hanocka, Raja Giryes, Daniel Cohen-Or
We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud.
1 code implementation • CVPR 2019 • Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, Daniel Cohen-Or
Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.
1 code implementation • 16 Sep 2018 • Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
In this paper, we utilize the unique properties of the mesh for a direct analysis of 3D shapes using MeshCNN, a convolutional neural network designed specifically for triangular meshes.
1 code implementation • 23 Apr 2018 • Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman, Daniel Cohen-Or
The process of aligning a pair of shapes is a fundamental operation in computer graphics.
no code implementations • 4 Feb 2017 • Naftali Zon, Rana Hanocka, Nahum Kiryati
PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring.