Search Results for author: Daniel Cohen-Or

Found 93 papers, 49 papers with code

Cross-Domain Cascaded Deep Translation

no code implementations ECCV 2020 Oren Katzir, Dani Lischinski, Daniel Cohen-Or

We mitigate this by descending the deep layers of a pre-trained network, where the deep features contain more semantics, and applying the translation between these deep features.

Image-to-Image Translation Translation

Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons

no code implementations3 Apr 2022 Oren Katzir, Dani Lischinski, Daniel Cohen-Or

We introduce an unsupervised technique for encoding point clouds into a canonical shape representation, by disentangling shape and pose.

Disentanglement Translation

MyStyle: A Personalized Generative Prior

no code implementations31 Mar 2022 Yotam Nitzan, Kfir Aberman, Qiurui He, Orly Liba, Michal Yarom, Yossi Gandelsman, Inbar Mosseri, Yael Pritch, Daniel Cohen-Or

Given a small reference set of portrait images of a person (~100), we tune the weights of a pretrained StyleGAN face generator to form a local, low-dimensional, personalized manifold in the latent space.

Image Enhancement Super-Resolution

MotionCLIP: Exposing Human Motion Generation to CLIP Space

1 code implementation15 Mar 2022 Guy Tevet, Brian Gordon, Amir Hertz, Amit H. Bermano, Daniel Cohen-Or

MotionCLIP gains its unique power by aligning its latent space with that of the Contrastive Language-Image Pre-training (CLIP) model.


State-of-the-Art in the Architecture, Methods and Applications of StyleGAN

no code implementations28 Feb 2022 Amit H. Bermano, Rinon Gal, Yuval Alaluf, Ron Mokady, Yotam Nitzan, Omer Tov, Or Patashnik, Daniel Cohen-Or

Of these, StyleGAN offers a fascinating case study, owing to its remarkable visual quality and an ability to support a large array of downstream tasks.

Image Generation

Self-Distilled StyleGAN: Towards Generation from Internet Photos

1 code implementation24 Feb 2022 Ron Mokady, Michal Yarom, Omer Tov, Oran Lang, Daniel Cohen-Or, Tali Dekel, Michal Irani, Inbar Mosseri

To meet these challenges, we proposed a StyleGAN-based self-distillation approach, which consists of two main components: (i) A generative-based self-filtering of the dataset to eliminate outlier images, in order to generate an adequate training set, and (ii) Perceptual clustering of the generated images to detect the inherent data modalities, which are then employed to improve StyleGAN's "truncation trick" in the image synthesis process.

Image Generation

Multi-level Latent Space Structuring for Generative Control

no code implementations11 Feb 2022 Oren Katzir, Vicky Perepelook, Dani Lischinski, Daniel Cohen-Or

Truncation is widely used in generative models for improving the quality of the generated samples, at the expense of reducing their diversity.

CLIPasso: Semantically-Aware Object Sketching

1 code implementation11 Feb 2022 Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Christian Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir

Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings.

Self-Conditioned Generative Adversarial Networks for Image Editing

no code implementations8 Feb 2022 Yunzhe Liu, Rinon Gal, Amit H. Bermano, Baoquan Chen, Daniel Cohen-Or

We compare our models to a wide range of latent editing methods, and show that by alleviating the bias they achieve finer semantic control and better identity preservation through a wider range of transformations.


FEAT: Face Editing with Attention

no code implementations6 Feb 2022 Xianxu Hou, Linlin Shen, Or Patashnik, Daniel Cohen-Or, Hui Huang

In this paper, we build on the StyleGAN generator, and present a method that explicitly encourages face manipulation to focus on the intended regions by incorporating learned attention maps.


SPAGHETTI: Editing Implicit Shapes Through Part Aware Generation

1 code implementation31 Jan 2022 Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or

Neural implicit fields are quickly emerging as an attractive representation for learning based techniques.

3D Shape Modeling

Third Time's the Charm? Image and Video Editing with StyleGAN3

1 code implementation31 Jan 2022 Yuval Alaluf, Or Patashnik, Zongze Wu, Asif Zamir, Eli Shechtman, Dani Lischinski, Daniel Cohen-Or

In particular, we demonstrate that while StyleGAN3 can be trained on unaligned data, one can still use aligned data for training, without hindering the ability to generate unaligned imagery.

Disentanglement Image Generation

ShapeFormer: Transformer-based Shape Completion via Sparse Representation

no code implementations25 Jan 2022 Xingguang Yan, Liqiang Lin, Niloy J. Mitra, Dani Lischinski, Daniel Cohen-Or, Hui Huang

We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds.

Stitch it in Time: GAN-Based Facial Editing of Real Videos

1 code implementation20 Jan 2022 Rotem Tzaban, Ron Mokady, Rinon Gal, Amit H. Bermano, Daniel Cohen-Or

The ability of Generative Adversarial Networks to encode rich semantics within their latent space has been widely adopted for facial image editing.

Facial Editing

DeepMLS: Geometry-Aware Control Point Deformation

no code implementations5 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.

Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure

no code implementations23 Nov 2021 Andreas Aristidou, Anastasios Yiannakidis, Kfir Aberman, Daniel Cohen-Or, Ariel Shamir, Yiorgos Chrysanthou

In this work, we present a music-driven motion synthesis framework that generates long-term sequences of human motions which are synchronized with the input beats, and jointly form a global structure that respects a specific dance genre.

motion synthesis

Mesh Draping: Parametrization-Free Neural Mesh Transfer

no code implementations11 Oct 2021 Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or

The method drapes the source mesh over the target geometry and at the same time seeks to preserve the carefully designed characteristics of the source mesh.

StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

2 code implementations2 Aug 2021 Rinon Gal, Or Patashnik, Haggai Maron, Gal Chechik, Daniel Cohen-Or

Can a generative model be trained to produce images from a specific domain, guided by a text prompt only, without seeing any image?

Domain Adaptation Image Manipulation

LARGE: Latent-Based Regression through GAN Semantics

1 code implementation22 Jul 2021 Yotam Nitzan, Rinon Gal, Ofir Brenner, Daniel Cohen-Or

For modern generative frameworks, this semantic encoding manifests as smooth, linear directions which affect image attributes in a disentangled manner.

StyleFusion: A Generative Model for Disentangling Spatial Segments

1 code implementation15 Jul 2021 Omer Kafri, Or Patashnik, Yuval Alaluf, Daniel Cohen-Or

Inserting the resulting style code into a pre-trained StyleGAN generator results in a single harmonized image in which each semantic region is controlled by one of the input latent codes.


JOKR: Joint Keypoint Representation for Unsupervised Cross-Domain Motion Retargeting

1 code implementation17 Jun 2021 Ron Mokady, Rotem Tzaban, Sagie Benaim, Amit H. Bermano, Daniel Cohen-Or

To alleviate this problem, we introduce JOKR - a JOint Keypoint Representation that captures the motion common to both the source and target videos, without requiring any object prior or data collection.

Disentanglement motion retargeting

Pivotal Tuning for Latent-based Editing of Real Images

2 code implementations10 Jun 2021 Daniel Roich, Ron Mokady, Amit H. Bermano, Daniel Cohen-Or

The key idea is pivotal tuning - a brief training process that preserves the editing quality of an in-domain latent region, while changing its portrayed identity and appearance.

Facial Editing Image Manipulation

MoCo-Flow: Neural Motion Consensus Flow for Dynamic Humans in Stationary Monocular Cameras

no code implementations8 Jun 2021 Xuelin Chen, Weiyu Li, Daniel Cohen-Or, Niloy J. Mitra, Baoquan Chen

In this paper, we introduce Neural Motion Consensus Flow (MoCo-Flow), a representation that models dynamic humans in stationary monocular cameras using a 4D continuous time-variant function.

Consistent Two-Flow Network for Tele-Registration of Point Clouds

1 code implementation1 Jun 2021 Zihao Yan, Zimu Yi, Ruizhen Hu, Niloy J. Mitra, Daniel Cohen-Or, Hui Huang

In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration.

FLEX: Extrinsic Parameter-free Multi-view 3D Human Motion Reconstruction

1 code implementation5 May 2021 Brian Gordon, Sigal Raab, Guy Azov, Raja Giryes, Daniel Cohen-Or

We compare our model to state-of-the-art methods that are not ep-free and show that in the absence of camera parameters, we outperform them by a large margin while obtaining comparable results when camera parameters are available.

3D Human Pose Estimation

Orienting Point Clouds with Dipole Propagation

1 code implementation4 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.

SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization

no code implementations NeurIPS 2021 Amir Hertz, Or Perel, Raja Giryes, Olga Sorkine-Hornung, Daniel Cohen-Or

Multilayer-perceptrons (MLP) are known to struggle with learning functions of high-frequencies, and in particular cases with wide frequency bands.

Representation Learning

ReStyle: A Residual-Based StyleGAN Encoder via Iterative Refinement

1 code implementation ICCV 2021 Yuval Alaluf, Or Patashnik, Daniel Cohen-Or

Instead of directly predicting the latent code of a given real image using a single pass, the encoder is tasked with predicting a residual with respect to the current estimate of the inverted latent code in a self-correcting manner.

Image Generation Real-to-Cartoon translation

StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery

4 code implementations ICCV 2021 Or Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or, Dani Lischinski

Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images.

 Ranked #1 on Image Manipulation on 10-Monty-Hall (using extra training data)

Image Manipulation

Clusterplot: High-dimensional Cluster Visualization

no code implementations4 Mar 2021 Or Malkai, Min Lu, Daniel Cohen-Or

We present Clusterplot, a multi-class high-dimensional data visualization tool designed to visualize cluster-level information offering an intuitive understanding of the cluster inter-relations.

Data Visualization Graphics

SWAGAN: A Style-based Wavelet-driven Generative Model

2 code implementations11 Feb 2021 Rinon Gal, Dana Cohen, Amit Bermano, Daniel Cohen-Or

In recent years, considerable progress has been made in the visual quality of Generative Adversarial Networks (GANs).

Only a Matter of Style: Age Transformation Using a Style-Based Regression Model

2 code implementations4 Feb 2021 Yuval Alaluf, Or Patashnik, Daniel Cohen-Or

In this formulation, our method approaches the continuous aging process as a regression task between the input age and desired target age, providing fine-grained control over the generated image.

Face Age Editing Image Manipulation +1

Designing an Encoder for StyleGAN Image Manipulation

4 code implementations4 Feb 2021 Omer Tov, Yuval Alaluf, Yotam Nitzan, Or Patashnik, Daniel Cohen-Or

We then suggest two principles for designing encoders in a manner that allows one to control the proximity of the inversions to regions that StyleGAN was originally trained on.

Image Manipulation

BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal Transfer

1 code implementation5 Oct 2020 Or Patashnik, Dov Danon, Hao Zhang, Daniel Cohen-Or

State-of-the-art image-to-image translation methods tend to struggle in an imbalanced domain setting, where one image domain lacks richness and diversity.

Image-to-Image Translation Style Transfer +1

Neural Alignment for Face De-pixelization

no code implementations29 Sep 2020 Maayan Shuvi, Noa Fish, Kfir Aberman, Ariel Shamir, Daniel Cohen-Or

Although simple, our framework synthesizes high-quality face reconstructions, demonstrating that given the statistical prior of a human face, multiple aligned pixelated frames contain sufficient information to reconstruct a high-quality approximation of the original signal.


SketchPatch: Sketch Stylization via Seamless Patch-level Synthesis

1 code implementation4 Sep 2020 Noa Fish, Lilach Perry, Amit Bermano, Daniel Cohen-Or

The paradigm of image-to-image translation is leveraged for the benefit of sketch stylization via transfer of geometric textural details.

Image-to-Image Translation Translation

Object Properties Inferring from and Transfer for Human Interaction Motions

no code implementations20 Aug 2020 Qian Zheng, Weikai Wu, Hanting Pan, Niloy Mitra, Daniel Cohen-Or, Hui Huang

In this paper, we present a fine-grained action recognition method that learns to infer such latent object properties from human interaction motion alone.

Fine-grained Action Recognition

Self-Sampling for Neural Point Cloud Consolidation

1 code implementation14 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.

MRGAN: Multi-Rooted 3D Shape Generation with Unsupervised Part Disentanglement

no code implementations25 Jul 2020 Rinon Gal, Amit Bermano, Hao Zhang, Daniel Cohen-Or

Our network encourages disentangled generation of semantic parts via two key ingredients: a root-mixing training strategy which helps decorrelate the different branches to facilitate disentanglement, and a set of loss terms designed with part disentanglement and shape semantics in mind.

3D Shape Generation Disentanglement

Deep Geometric Texture Synthesis

1 code implementation30 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.

Image Generation Texture Synthesis

DO-Conv: Depthwise Over-parameterized Convolutional Layer

1 code implementation22 Jun 2020 Jinming Cao, Yangyan Li, Mingchao Sun, Ying Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen, Changhe Tu

Moreover, in the inference phase, the depthwise convolution is folded into the conventional convolution, reducing the computation to be exactly equivalent to that of a convolutional layer without over-parameterization.

Image Classification

MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency

no code implementations22 Jun 2020 Mingyi Shi, Kfir Aberman, Andreas Aristidou, Taku Komura, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen

We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.

Towards a Neural Graphics Pipeline for Controllable Image Generation

no code implementations18 Jun 2020 Xuelin Chen, Daniel Cohen-Or, Baoquan Chen, Niloy J. Mitra

NGP decomposes the image into a set of interpretable appearance feature maps, uncovering direct control handles for controllable image generation.

Image Generation Neural Rendering

Point2Mesh: A Self-Prior for Deformable Meshes

2 code implementations22 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.

Face Identity Disentanglement via Latent Space Mapping

3 code implementations15 May 2020 Yotam Nitzan, Amit Bermano, Yangyan Li, Daniel Cohen-Or

Learning disentangled representations of data is a fundamental problem in artificial intelligence.

De-identification Disentanglement

Skeleton-Aware Networks for Deep Motion Retargeting

1 code implementation12 May 2020 Kfir Aberman, Peizhuo Li, Dani Lischinski, Olga Sorkine-Hornung, Daniel Cohen-Or, Baoquan Chen

In other words, our operators form the building blocks of a new deep motion processing framework that embeds the motion into a common latent space, shared by a collection of homeomorphic skeletons.

motion retargeting motion synthesis

Unpaired Motion Style Transfer from Video to Animation

1 code implementation12 May 2020 Kfir Aberman, Yijia Weng, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen

In this paper, we present a novel data-driven framework for motion style transfer, which learns from an unpaired collection of motions with style labels, and enables transferring motion styles not observed during training.

3D Reconstruction motion style transfer +1

Image Morphing with Perceptual Constraints and STN Alignment

1 code implementation29 Apr 2020 Noa Fish, Richard Zhang, Lilach Perry, Daniel Cohen-Or, Eli Shechtman, Connelly Barnes

In image morphing, a sequence of plausible frames are synthesized and composited together to form a smooth transformation between given instances.

Frame Image Morphing

Single Pair Cross-Modality Super Resolution

no code implementations CVPR 2021 Guy Shacht, Sharon Fogel, Dov Danon, Daniel Cohen-Or, Ilya Leizerson

The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.


Structural-analogy from a Single Image Pair

1 code implementation5 Apr 2020 Sagie Benaim, Ron Mokady, Amit Bermano, Daniel Cohen-Or, Lior Wolf

In this paper, we explore the capabilities of neural networks to understand image structure given only a single pair of images, A and B.

Translation Unsupervised Image-To-Image Translation

PointGMM: a Neural GMM Network for Point Clouds

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.

Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation

1 code implementation CVPR 2020 Moab Arar, Yiftach Ginger, Dov Danon, Ilya Leizerson, Amit Bermano, Daniel Cohen-Or

In this work, we bypass the difficulties of developing cross-modality similarity measures, by training an image-to-image translation network on the two input modalities.

Autonomous Driving Image Registration +2

A Rotation-Invariant Framework for Deep Point Cloud Analysis

1 code implementation16 Mar 2020 Xianzhi Li, Ruihui Li, Guangyong Chen, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

Recently, many deep neural networks were designed to process 3D point clouds, but a common drawback is that rotation invariance is not ensured, leading to poor generalization to arbitrary orientations.

Point Cloud Generation

GANHopper: Multi-Hop GAN for Unsupervised Image-to-Image Translation

1 code implementation ECCV 2020 Wallace Lira, Johannes Merz, Daniel Ritchie, Daniel Cohen-Or, Hao Zhang

Instead of executing translation directly, we steer the translation by requiring the network to produce in-between images that resemble weighted hybrids between images from the input domains.

Translation Unsupervised Image-To-Image Translation

Unsupervised multi-modal Styled Content Generation

no code implementations10 Jan 2020 Omry Sendik, Dani Lischinski, Daniel Cohen-Or

The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D.

Cross-Domain Cascaded Deep Feature Translation

no code implementations4 Jun 2019 Oren Katzir, Dani Lischinski, Daniel Cohen-Or

Our translation is performed in a cascaded, deep-to-shallow, fashion, along the deep feature hierarchy: we first translate between the deepest layers that encode the higher-level semantic content of the image, proceeding to translate the shallower layers, conditioned on the deeper ones.

Image-to-Image Translation Translation

Unsupervised Detection of Distinctive Regions on 3D Shapes

no code implementations5 May 2019 Xianzhi Li, Lequan Yu, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

This paper presents a novel approach to learn and detect distinctive regions on 3D shapes.

Learning Character-Agnostic Motion for Motion Retargeting in 2D

2 code implementations5 May 2019 Kfir Aberman, Rundi Wu, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or

In order to achieve our goal, we learn to extract, directly from a video, a high-level latent motion representation, which is invariant to the skeleton geometry and the camera view.

3D Reconstruction motion retargeting

Image Resizing by Reconstruction from Deep Features

no code implementations17 Apr 2019 Moab Arar, Dov Danon, Daniel Cohen-Or, Ariel Shamir

In this paper we perform image resizing in feature space where the deep layers of a neural network contain rich important semantic information.

Implicit Pairs for Boosting Unpaired Image-to-Image Translation

no code implementations15 Apr 2019 Yiftach Ginger, Dov Danon, Hadar Averbuch-Elor, Daniel Cohen-Or

As a result, in recent years more attention has been given to techniques that learn the mapping from unpaired sets.

14 Image-to-Image Translation +1

Blind Visual Motif Removal from a Single Image

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.

LOGAN: Unpaired Shape Transform in Latent Overcomplete Space

no code implementations25 Mar 2019 Kangxue Yin, Zhiqin Chen, Hui Huang, Daniel Cohen-Or, Hao Zhang

Our network consists of an autoencoder to encode shapes from the two input domains into a common latent space, where the latent codes concatenate multi-scale shape features, resulting in an overcomplete representation.


CrossNet: Latent Cross-Consistency for Unpaired Image Translation

no code implementations14 Jan 2019 Omry Sendik, Dani Lischinski, Daniel Cohen-Or

Recent GAN-based architectures have been able to deliver impressive performance on the general task of image-to-image translation.

Image-to-Image Translation Translation

CompoNet: Learning to Generate the Unseen by Part Synthesis and Composition

1 code implementation ICCV 2019 Nadav Schor, Oren Katzir, Hao Zhang, Daniel Cohen-Or

Data-driven generative modeling has made remarkable progress by leveraging the power of deep neural networks.

Generative Low-Shot Network Expansion

no code implementations ICLR 2018 Adi Hayat, Mark Kliger, Shachar Fleishman, Daniel Cohen-Or

We present a simple yet powerful hard distillation method where the base network is augmented with additional weights to classify the novel classes, while keeping the weights of the base network unchanged.

MeshCNN: A Network with an Edge

1 code implementation16 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.

3D Part Segmentation Cube Engraving Classification

Multi-Scale Context Intertwining for Semantic Segmentation

no code implementations ECCV 2018 Di Lin, Yuanfeng Ji, Dani Lischinski, Daniel Cohen-Or, Hui Huang

Accurate semantic image segmentation requires the joint consideration of local appearance, semantic information, and global scene context.

Semantic Segmentation

Deep Video-Based Performance Cloning

no code implementations21 Aug 2018 Kfir Aberman, Mingyi Shi, Jing Liao, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or

After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor, we are able to generate videos where this actor reenacts other performances.

Structure-aware Generative Network for 3D-Shape Modeling

1 code implementation12 Aug 2018 Zhijie Wu, Xiang Wang, Di Lin, Dani Lischinski, Daniel Cohen-Or, Hui Huang

The key idea is that during the analysis, the two branches exchange information between them, thereby learning the dependencies between structure and geometry and encoding two augmented features, which are then fused into a single latent code.


GRAINS: Generative Recursive Autoencoders for INdoor Scenes

no code implementations24 Jul 2018 Manyi Li, Akshay Gadi Patil, Kai Xu, Siddhartha Chaudhuri, Owais Khan, Ariel Shamir, Changhe Tu, Baoquan Chen, Daniel Cohen-Or, Hao Zhang

We present a generative neural network which enables us to generate plausible 3D indoor scenes in large quantities and varieties, easily and highly efficiently.


EC-Net: an Edge-aware Point set Consolidation Network

no code implementations ECCV 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

In this paper, we present the first deep learning based edge-aware technique to facilitate the consolidation of point clouds.

Surface Reconstruction

Non-Stationary Texture Synthesis by Adversarial Expansion

1 code implementation11 May 2018 Yang Zhou, Zhen Zhu, Xiang Bai, Dani Lischinski, Daniel Cohen-Or, Hui Huang

We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar.

Texture Synthesis

Neural Best-Buddies: Sparse Cross-Domain Correspondence

2 code implementations10 May 2018 Kfir Aberman, Jing Liao, Mingyi Shi, Dani Lischinski, Baoquan Chen, Daniel Cohen-Or

Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications.

Image Morphing

ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learning

1 code implementation23 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.

P2P-NET: Bidirectional Point Displacement Net for Shape Transform

no code implementations25 Mar 2018 Kangxue Yin, Hui Huang, Daniel Cohen-Or, Hao Zhang

We introduce P2P-NET, a general-purpose deep neural network which learns geometric transformations between point-based shape representations from two domains, e. g., meso-skeletons and surfaces, partial and complete scans, etc.

Clustering-driven Deep Embedding with Pairwise Constraints

1 code implementation22 Mar 2018 Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger, Daniel Cohen-Or

In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.

Outlier Detection for Robust Multi-dimensional Scaling

no code implementations7 Feb 2018 Leonid Blouvshtein, Daniel Cohen-Or

Multi-dimensional scaling (MDS) plays a central role in data-exploration, dimensionality reduction and visualization.

Dimensionality Reduction Outlier Detection

PU-Net: Point Cloud Upsampling Network

3 code implementations CVPR 2018 Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, Pheng-Ann Heng

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data.

Point Cloud Super Resolution

Neuron-level Selective Context Aggregation for Scene Segmentation

no code implementations22 Nov 2017 Zhenhua Wang, Fanglin Gu, Dani Lischinski, Daniel Cohen-Or, Changhe Tu, Baoquan Chen

Contextual information provides important cues for disambiguating visually similar pixels in scene segmentation.

Scene Segmentation

Cascaded Feature Network for Semantic Segmentation of RGB-D Images

no code implementations ICCV 2017 Di Lin, Guangyong Chen, Daniel Cohen-Or, Pheng-Ann Heng, Hui Huang

Our approach is to use the available depth to split the image into layers with common visual characteristic of objects/scenes, or common "scene-resolution".

Semantic Segmentation

Bundle Optimization for Multi-aspect Embedding

no code implementations29 Mar 2017 Qiong Zeng, Baoquan Chen, Yanir Kleiman, Daniel Cohen-Or, Yangyan Li

Understanding semantic similarity among images is the core of a wide range of computer vision applications.

Image Classification Semantic Similarity +1

Co-segmentation for Space-Time Co-located Collections

no code implementations31 Jan 2017 Hadar Averbuch-Elor, Johannes Kopf, Tamir Hazan, Daniel Cohen-Or

Thus, to disambiguate what the common foreground object is, we introduce a weakly-supervised technique, where we assume only a small seed, given in the form of a single segmented image.

Border-Peeling Clustering

1 code implementation14 Dec 2016 Hadar Averbuch-Elor, Nadav Bar, Daniel Cohen-Or

In this paper, we present a novel non-parametric clustering technique.

A Holistic Approach for Data-Driven Object Cutout

no code implementations18 Aug 2016 Huayong Xu, Yangyan Li, Wenzheng Chen, Dani Lischinski, Daniel Cohen-Or, Baoquan Chen

We show that the resulting P-maps may be used to evaluate how likely a rectangle proposal is to contain an instance of the class, and further process good proposals to produce an accurate object cutout mask.

Spherical Embedding of Inlier Silhouette Dissimilarities

no code implementations CVPR 2015 Etai Littwin, Hadar Averbuch-Elor, Daniel Cohen-Or

In this paper, we introduce a spherical embedding technique to position a given set of silhouettes of an object as observed from a set of cameras arbitrarily positioned around the object.

Constraints as Features

no code implementations CVPR 2013 Shmuel Asafi, Daniel Cohen-Or

In this paper, we introduce a new approach to constrained clustering which treats the constraints as features.

Semantic Segmentation

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