Research

Our research is focused on building artificial intelligence for 3D data, spanning the fields of computer graphics, machine learning, and computer vision. Deep Learning, the most popular form of artificial intelligence, has unlocked remarkable success on structured data (such as text, images, and video), and we are interested in harnessing the potential of these techniques to enable effective operation on unstructured 3D geometric data.

We have developed a convolutional neural network designed specifically for meshes, and also explored how to learn from the internal data within a single shape (for surface reconstruction, geometric texture synthesis, and point cloud consolidation) - and are interested in broader applications related to these areas. Additional research directions that we are aiming to explore include: intertwining human and machine-based creativity to advance our capabilities in 3D shape modeling and animation; learning with less supervision, for example to extract patterns and relationships from large shape collections; and making 3D neural networks more interpretable and explainable.

Follow our github organization for updates on our latest research.

3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation

Dale Decatur, Itai Lang, Kfir Aberman, and Rana Hanocka

CVPR 2024

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HyperFields:Towards Zero-Shot Generation of NeRFs from Text

Sudarshan Babu*, Richard Liu*, Avery Zhou*, Michael Maire, Greg Shakhnarovich, and Rana Hanocka

ArXiv 2023

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ShapeSonic: Sonifying Fingertip Interactions for Non-Visual Virtual Shape Perception

Jialin Huang, Rana Hanocka, Alexa Siu, and Yotam Gingold

SIGGRAPH Asia 2023

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TextDeformer: Geometry Manipulation using Text Guidance

William Gao, Noam Aigerman, Thibault Groueix, Vladimir G. Kim, and Rana Hanocka

SIGGRAPH 2023

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DA Wand: Distortion-Aware Selection using Neural Mesh Parameterization

Richard Liu, Noam Aigerman, Vladimir G. Kim, and Rana Hanocka

CVPR 2023

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3D Highlighter: Localizing Regions on 3D Shapes via Text Descriptions

Dale Decatur, Itai Lang, and Rana Hanocka

CVPR 2023

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GLAZE: Protecting Artists from Style Mimicry by Text-to-Image Models

Shawn Shan, Jenna Cryan, Emily Wenger, Haitao Zheng, Rana Hanocka, Ben Y. Zhao

USENIX 2023 (Distinguished Paper Award)

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TetGAN: A Convolutional Neural Network for Tetrahedral Mesh Generation

William Gao, April Wang, Gal Metzer, Raymond A. Yeh, and Rana Hanocka

BMVC 2022 (Oral)

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GANimator: Neural Motion Synthesis from a Single Sequence

Peizhuo Li, Kfir Aberman, Zihan Zhang, Rana Hanocka and Olga Sorkine-Hornung

SIGGRAPH 2022

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Text2Mesh: Text-Driven Neural Stylization for Meshes

Oscar Michel*, Roi Bar-On*, Richard Liu*, Sagie Benaim, and Rana Hanocka (* equal contribution)

CVPR 2022 (Oral)

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The Neurally-Guided Shape Parser: Grammar-based Labeling of 3D Shape Regions with Approximate Inference

Russel K. Jones, Aalia Habib, Rana Hanocka, and Daniel Ritchie

CVPR 2022

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Z2P: Instant Visualization of Point Clouds

Gal Metzer, Rana Hanocka, Raja Giryes, Niloy J. Mitra, and Daniel Cohen-Or

Eurographics 2022

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NeuralMLS: Geometry-Aware Control Point Deformation

Meitar Shechter, Rana Hanocka, Gal Metzer, Raja Giryes, and Daniel Cohen-Or

Eurographics Short Paper 2022

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Orienting Point Clouds With Dipole Propagation

Gal Metzer, Rana Hanocka, Denis Zorin, Raja Giryes, Daniele Panozzo, and Daniel Cohen-Or

ACM Transactions on Graphics (Proc. SIGGRAPH) 2021

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Learning Skeletal Articulations With Neural Blend Shapes

Peizhuo Li, Kfir Aberman, Rana Hanocka, Libin Liu, Olga Sorkine-Hornung, and Baoquan Chen

ACM Transactions on Graphics (Proc. SIGGRAPH) 2021

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Self-Sampling for Neural Point Cloud Consolidation

Gal Metzer, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or

ACM Transactions on Graphics (TOG) 2021

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Point2Mesh: A Self-Prior for Deformable Meshes

Rana Hanocka, Gal Metzer, Raja Giryes, and Daniel Cohen-Or

ACM Transactions on Graphics (Proc. SIGGRAPH) 2020

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Deep Geometric Texture Synthesis

Amir Hertz*, Rana Hanocka*, Raja Giryes, and Daniel Cohen-Or (* joint first authors)

ACM Transactions on Graphics (Proc. SIGGRAPH) 2020

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PointGMM: a Neural GMM Network for Point Clouds

Amir Hertz, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or

CVPR 2020

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MeshCNN: A Network with an Edge

Rana Hanocka, Amir Hertz, Noa Fish, Raja Giryes, Shachar Fleishman and Daniel Cohen-Or

ACM Transactions on Graphics (Proc. SIGGRAPH) 2019

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Blind Visual Motif Removal from a Single Image

Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or

CVPR 2019 (Oral)

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ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

Rana Hanocka, Noa Fish, Zhenhua Wang, Raja Giryes, Shachar Fleishman and Daniel Cohen-Or

ACM Transactions on Graphics (TOG) 2018

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Fast and Easy Blind Deblurring Using an Inverse Filter and PROBE

Naftali Zon*, Rana Hanocka*, Nahum Kiryati (* joint first authors)

Proc. 17th International Conference on Computer Analysis of Images and Patterns (CAIP 2017)

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Progressive Blind Deconvolution

Rana Hanocka, Nahum Kiryati

Proc. 16th International Conference on Computer Analysis of Images and Patterns (CAIP 2015)

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