2 code implementations • 3 Feb 2025 • Rohit Gandikota, Zongze Wu, Richard Zhang, David Bau, Eli Shechtman, Nick Kolkin
Unlike existing control methods that require a user to specify attributes for each edit direction individually, SliderSpace discovers multiple interpretable and diverse directions simultaneously from a single text prompt.
no code implementations • 22 Dec 2024 • Haoran You, Connelly Barnes, Yuqian Zhou, Yan Kang, Zhenbang Du, Wei Zhou, Lingzhi Zhang, Yotam Nitzan, Xiaoyang Liu, Zhe Lin, Eli Shechtman, Sohrab Amirghodsi, Yingyan Celine Lin
To address this, we propose DiffRatio-MoD, a dynamic DiT inference framework with differentiable compression ratios, which automatically learns to dynamically route computation across layers and timesteps for each image token, resulting in Mixture-of-Depths (MoD) efficient DiT models.
no code implementations • 10 Dec 2024 • Tianwei Yin, Qiang Zhang, Richard Zhang, William T. Freeman, Fredo Durand, Eli Shechtman, Xun Huang
Current video diffusion models achieve impressive generation quality but struggle in interactive applications due to bidirectional attention dependencies.
no code implementations • 1 Oct 2024 • Yuheng Li, Haotian Liu, Mu Cai, Yijun Li, Eli Shechtman, Zhe Lin, Yong Jae Lee, Krishna Kumar Singh
In this paper, we introduce a model designed to improve the prediction of image-text alignment, targeting the challenge of compositional understanding in current visual-language models.
no code implementations • 14 Aug 2024 • Zongze Wu, Nicholas Kolkin, Jonathan Brandt, Richard Zhang, Eli Shechtman
We address the challenges of precise image inversion and disentangled image editing in the context of few-step diffusion models.
no code implementations • CVPR 2024 • Yinbo Chen, Oliver Wang, Richard Zhang, Eli Shechtman, Xiaolong Wang, Michael Gharbi
We propose to learn the distribution of continuous images by training diffusion models on image neural fields, which can be rendered at any resolution, and show its advantages over fixed-resolution models.
1 code implementation • 23 May 2024 • Tianwei Yin, Michaël Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman
Recent approaches have shown promises distilling diffusion models into efficient one-step generators.
no code implementations • 9 May 2024 • Minguk Kang, Richard Zhang, Connelly Barnes, Sylvain Paris, Suha Kwak, Jaesik Park, Eli Shechtman, Jun-Yan Zhu, Taesung Park
We propose a method to distill a complex multistep diffusion model into a single-step conditional GAN student model, dramatically accelerating inference, while preserving image quality.
no code implementations • 24 Apr 2024 • Jiteng Mu, Michaël Gharbi, Richard Zhang, Eli Shechtman, Nuno Vasconcelos, Xiaolong Wang, Taesung Park
In this work, we propose an image representation that promotes spatial editing of input images using a diffusion model.
no code implementations • 18 Apr 2024 • Yotam Nitzan, Zongze Wu, Richard Zhang, Eli Shechtman, Daniel Cohen-Or, Taesung Park, Michaël Gharbi
We demonstrate that our approach is competitive with state-of-the-art inpainting methods in terms of quality and fidelity while providing a 10x speedup for typical user interactions, where the editing mask represents 10% of the image.
no code implementations • 18 Apr 2024 • Nupur Kumari, Grace Su, Richard Zhang, Taesung Park, Eli Shechtman, Jun-Yan Zhu
In this work, we introduce a new task -- enabling explicit control of the object viewpoint in the customization of text-to-image diffusion models.
no code implementations • 18 Apr 2024 • Yiran Xu, Taesung Park, Richard Zhang, Yang Zhou, Eli Shechtman, Feng Liu, Jia-Bin Huang, Difan Liu
We introduce VideoGigaGAN, a new generative VSR model that can produce videos with high-frequency details and temporal consistency.
Ranked #16 on
Video Super-Resolution
on Vid4 - 4x upscaling
(PSNR metric)
no code implementations • 19 Mar 2024 • Hadi AlZayer, Zhihao Xia, Xuaner Zhang, Eli Shechtman, Jia-Bin Huang, Michael Gharbi
We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects.
no code implementations • 9 Jan 2024 • Xiaojuan Wang, Taesung Park, Yang Zhou, Eli Shechtman, Richard Zhang
We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks.
no code implementations • CVPR 2024 • Mang Tik Chiu, Yuqian Zhou, Lingzhi Zhang, Zhe Lin, Connelly Barnes, Sohrab Amirghodsi, Eli Shechtman, Humphrey Shi
Object inpainting is a task that involves adding objects to real images and seamlessly compositing them.
no code implementations • 7 Dec 2023 • Joanna Materzynska, Josef Sivic, Eli Shechtman, Antonio Torralba, Richard Zhang, Bryan Russell
To avoid overfitting to the new custom motion, we introduce an approach for regularization over videos.
2 code implementations • CVPR 2024 • Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T. Freeman, Taesung Park
We introduce Distribution Matching Distillation (DMD), a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality.
1 code implementation • ICCV 2023 • Lingzhi Zhang, Zhengjie Xu, Connelly Barnes, Yuqian Zhou, Qing Liu, He Zhang, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo Shi
Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks.
no code implementations • 9 Jul 2023 • Dan Ruta, Gemma Canet Tarrés, Andrew Gilbert, Eli Shechtman, Nicholas Kolkin, John Collomosse
Neural Style Transfer (NST) is the field of study applying neural techniques to modify the artistic appearance of a content image to match the style of a reference style image.
1 code implementation • CVPR 2023 • S. Mahdi H. Miangoleh, Zoya Bylinskii, Eric Kee, Eli Shechtman, Yağız Aksoy
We thus offer a viable solution for automating image enhancement and photo cleanup operations.
1 code implementation • CVPR 2023 • Chuong Huynh, Yuqian Zhou, Zhe Lin, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, Abhinav Shrivastava
In photo editing, it is common practice to remove visual distractions to improve the overall image quality and highlight the primary subject.
1 code implementation • 11 Apr 2023 • Dan Ruta, Andrew Gilbert, John Collomosse, Eli Shechtman, Nicholas Kolkin
As a component of curating this data, we present a novel model able to classify if an image is stylistic.
1 code implementation • CVPR 2023 • Mang Tik Chiu, Xuaner Zhang, Zijun Wei, Yuqian Zhou, Eli Shechtman, Connelly Barnes, Zhe Lin, Florian Kainz, Sohrab Amirghodsi, Humphrey Shi
In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds.
2 code implementations • ICCV 2023 • Nupur Kumari, Bingliang Zhang, Sheng-Yu Wang, Eli Shechtman, Richard Zhang, Jun-Yan Zhu
To achieve this goal, we propose an efficient method of ablating concepts in the pretrained model, i. e., preventing the generation of a target concept.
1 code implementation • CVPR 2023 • Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, Taesung Park
From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models.
Ranked #16 on
Text-to-Image Generation
on MS COCO
no code implementations • CVPR 2023 • Ke Wang, Michaël Gharbi, He Zhang, Zhihao Xia, Eli Shechtman
Learning-based image harmonization techniques are usually trained to undo synthetic random global transformations applied to a masked foreground in a single ground truth photo.
2 code implementations • CVPR 2023 • Yotam Nitzan, Michaël Gharbi, Richard Zhang, Taesung Park, Jun-Yan Zhu, Daniel Cohen-Or, Eli Shechtman
First, we note the generator contains a meaningful, pretrained latent space.
no code implementations • 13 Dec 2022 • Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen, Eli Shechtman, Connelly Barnes, Jianming Zhang, Qing Liu, Yuqian Zhou, Sohrab Amirghodsi, Jiebo Luo
Moreover, the object-level discriminators take aligned instances as inputs to enforce the realism of individual objects.
2 code implementations • CVPR 2023 • Nupur Kumari, Bingliang Zhang, Richard Zhang, Eli Shechtman, Jun-Yan Zhu
Can we teach a model to quickly acquire a new concept, given a few examples?
no code implementations • 4 Nov 2022 • Yuheng Li, Yijun Li, Jingwan Lu, Eli Shechtman, Yong Jae Lee, Krishna Kumar Singh
We introduce a new method for diverse foreground generation with explicit control over various factors.
1 code implementation • 8 Sep 2022 • Xiaodan Du, Raymond A. Yeh, Nicholas Kolkin, Eli Shechtman, Greg Shakhnarovich
We propose Fast text2StyleGAN, a natural language interface that adapts pre-trained GANs for text-guided human face synthesis.
no code implementations • 6 Aug 2022 • Lingzhi Zhang, Connelly Barnes, Kevin Wampler, Sohrab Amirghodsi, Eli Shechtman, Zhe Lin, Jianbo Shi
Recently, deep models have established SOTA performance for low-resolution image inpainting, but they lack fidelity at resolutions associated with modern cameras such as 4K or more, and for large holes.
1 code implementation • 5 Aug 2022 • Lingzhi Zhang, Yuqian Zhou, Connelly Barnes, Sohrab Amirghodsi, Zhe Lin, Eli Shechtman, Jianbo Shi
Inspired by this workflow, we propose a new learning task of automatic segmentation of inpainting perceptual artifacts, and apply the model for inpainting model evaluation and iterative refinement.
no code implementations • 12 Jul 2022 • Yichen Sheng, Yifan Liu, Jianming Zhang, Wei Yin, A. Cengiz Oztireli, He Zhang, Zhe Lin, Eli Shechtman, Bedrich Benes
It can be used to calculate hard shadows in a 2D image based on the projective geometry, providing precise control of the shadows' direction and shape.
no code implementations • CVPR 2022 • ShahRukh Athar, Zexiang Xu, Kalyan Sunkavalli, Eli Shechtman, Zhixin Shu
In this work, we propose RigNeRF, a system that goes beyond just novel view synthesis and enables full control of head pose and facial expressions learned from a single portrait video.
1 code implementation • 13 Jun 2022 • Kai Zhang, Nick Kolkin, Sai Bi, Fujun Luan, Zexiang Xu, Eli Shechtman, Noah Snavely
We present a method for transferring the artistic features of an arbitrary style image to a 3D scene.
no code implementations • 5 May 2022 • Dave Epstein, Taesung Park, Richard Zhang, Eli Shechtman, Alexei A. Efros
Blobs are differentiably placed onto a feature grid that is decoded into an image by a generative adversarial network.
1 code implementation • 14 Apr 2022 • Lucy Chai, Michael Gharbi, Eli Shechtman, Phillip Isola, Richard Zhang
To take advantage of varied-size data, we introduce continuous-scale training, a process that samples patches at random scales to train a new generator with variable output resolutions.
1 code implementation • 24 Mar 2022 • Nicholas Kolkin, Michal Kucera, Sylvain Paris, Daniel Sykora, Eli Shechtman, Greg Shakhnarovich
We propose Neural Neighbor Style Transfer (NNST), a pipeline that offers state-of-the-art quality, generalization, and competitive efficiency for artistic style transfer.
1 code implementation • 22 Mar 2022 • Haitian Zheng, Zhe Lin, Jingwan Lu, Scott Cohen, Eli Shechtman, Connelly Barnes, Jianming Zhang, Ning Xu, Sohrab Amirghodsi, Jiebo Luo
We propose cascaded modulation GAN (CM-GAN), a new network design consisting of an encoder with Fourier convolution blocks that extract multi-scale feature representations from the input image with holes and a dual-stream decoder with a novel cascaded global-spatial modulation block at each scale level.
Ranked #3 on
Image Inpainting
on Places2
2 code implementations • CVPR 2022 • Anna Frühstück, Krishna Kumar Singh, Eli Shechtman, Niloy J. Mitra, Peter Wonka, Jingwan Lu
Instead of modeling this complex domain with a single GAN, we propose a novel method to combine multiple pretrained GANs, where one GAN generates a global canvas (e. g., human body) and a set of specialized GANs, or insets, focus on different parts (e. g., faces, shoes) that can be seamlessly inserted onto the global canvas.
1 code implementation • 31 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.
no code implementations • 20 Jan 2022 • Yunhan Zhao, Connelly Barnes, Yuqian Zhou, Eli Shechtman, Sohrab Amirghodsi, Charless Fowlkes
Our approach achieves state-of-the-art performance on both RealEstate10K and MannequinChallenge dataset with large baselines, complex geometry and extreme camera motions.
1 code implementation • CVPR 2022 • Roy Or-El, Xuan Luo, Mengyi Shan, Eli Shechtman, Jeong Joon Park, Ira Kemelmacher-Shlizerman
We introduce a high resolution, 3D-consistent image and shape generation technique which we call StyleSDF.
1 code implementation • CVPR 2022 • Nupur Kumari, Richard Zhang, Eli Shechtman, Jun-Yan Zhu
Can the collective "knowledge" from a large bank of pretrained vision models be leveraged to improve GAN training?
Ranked #1 on
Image Generation
on AFHQ Cat
1 code implementation • CVPR 2022 • William Peebles, Jun-Yan Zhu, Richard Zhang, Antonio Torralba, Alexei A. Efros, Eli Shechtman
We propose GAN-Supervised Learning, a framework for learning discriminative models and their GAN-generated training data jointly end-to-end.
1 code implementation • ICLR 2022 • Zongze Wu, Yotam Nitzan, Eli Shechtman, Dani Lischinski
Several works already utilize some basic properties of aligned StyleGAN models to perform image-to-image translation.
no code implementations • 20 Oct 2021 • David Futschik, Michal Kučera, Michal Lukáč, Zhaowen Wang, Eli Shechtman, Daniel Sýkora
We present an approach to example-based stylization of images that uses a single pair of a source image and its stylized counterpart.
no code implementations • NeurIPS Workshop SVRHM 2021 • Ard Kastrati, Zoya Bylinskii, Eli Shechtman
Dozens of saliency models have been designed over the last few decades, targeted at diverse applications ranging from image compression and retargeting to robot navigation, surveillance, and distractor detection.
1 code implementation • 12 Oct 2021 • David Futschik, Michal Lukáč, Eli Shechtman, Daniel Sýkora
In this short report, we present a simple, yet effective approach to editing real images via generative adversarial networks (GAN).
no code implementations • ICCV 2021 • Yuheng Li, Yijun Li, Jingwan Lu, Eli Shechtman, Yong Jae Lee, Krishna Kumar Singh
We propose a new approach for high resolution semantic image synthesis.
no code implementations • 13 Sep 2021 • Badour AlBahar, Jingwan Lu, Jimei Yang, Zhixin Shu, Eli Shechtman, Jia-Bin Huang
We present an algorithm for re-rendering a person from a single image under arbitrary poses.
1 code implementation • CVPR 2021 • Lucy Chai, Jun-Yan Zhu, Eli Shechtman, Phillip Isola, Richard Zhang
Here, we investigate whether such views can be applied to real images to benefit downstream analysis tasks such as image classification.
3 code implementations • CVPR 2021 • Utkarsh Ojha, Yijun Li, Jingwan Lu, Alexei A. Efros, Yong Jae Lee, Eli Shechtman, Richard Zhang
Training generative models, such as GANs, on a target domain containing limited examples (e. g., 10) can easily result in overfitting.
Ranked #3 on
10-shot image generation
on Babies
2 code implementations • ICCV 2021 • Ishit Mehta, Michaël Gharbi, Connelly Barnes, Eli Shechtman, Ravi Ramamoorthi, Manmohan Chandraker
Our approach produces generalizable functional representations of images, videos and shapes, and achieves higher reconstruction quality than prior works that are optimized for a single signal.
5 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.
no code implementations • CVPR 2021 • Yuqian Zhou, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi
Image inpainting is the task of plausibly restoring missing pixels within a hole region that is to be removed from a target image.
1 code implementation • 5 Feb 2021 • Tobias Hinz, Matthew Fisher, Oliver Wang, Eli Shechtman, Stefan Wermter
Our model generates novel poses based on keypoint locations, which can be modified in real time while providing interactive feedback, allowing for intuitive reposing and animation.
1 code implementation • CVPR 2021 • Tamar Rott Shaham, Michael Gharbi, Richard Zhang, Eli Shechtman, Tomer Michaeli
We introduce a new generator architecture, aimed at fast and efficient high-resolution image-to-image translation.
no code implementations • NeurIPS 2020 • Yijun Li, Richard Zhang, Jingwan Lu, Eli Shechtman
Few-shot image generation seeks to generate more data of a given domain, with only few available training examples.
Ranked #4 on
10-shot image generation
on Babies
6 code implementations • CVPR 2021 • Zongze Wu, Dani Lischinski, Eli Shechtman
Manipulation of visual attributes via these StyleSpace controls is shown to be better disentangled than via those proposed in previous works.
no code implementations • ECCV 2020 • Youssef Alami Mejjati, Celso F. Gomez, Kwang In Kim, Eli Shechtman, Zoya Bylinskii
Extensions of our model allow for multi-style edits and the ability to both increase and attenuate attention in an image region.
4 code implementations • NeurIPS 2020 • Taesung Park, Jun-Yan Zhu, Oliver Wang, Jingwan Lu, Eli Shechtman, Alexei A. Efros, Richard Zhang
Deep generative models have become increasingly effective at producing realistic images from randomly sampled seeds, but using such models for controllable manipulation of existing images remains challenging.
1 code implementation • ECCV 2020 • Yu Zeng, Zhe Lin, Jimei Yang, Jianming Zhang, Eli Shechtman, Huchuan Lu
To address this challenge, we propose an iterative inpainting method with a feedback mechanism.
Ranked #8 on
Image Inpainting
on Places2
1 code implementation • 29 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.
3 code implementations • 27 Apr 2020 • Yang Zhou, Xintong Han, Eli Shechtman, Jose Echevarria, Evangelos Kalogerakis, DIngzeyu Li
We present a method that generates expressive talking heads from a single facial image with audio as the only input.
no code implementations • 8 Apr 2020 • Ayush Tewari, Ohad Fried, Justus Thies, Vincent Sitzmann, Stephen Lombardi, Kalyan Sunkavalli, Ricardo Martin-Brualla, Tomas Simon, Jason Saragih, Matthias Nießner, Rohit Pandey, Sean Fanello, Gordon Wetzstein, Jun-Yan Zhu, Christian Theobalt, Maneesh Agrawala, Eli Shechtman, Dan B. Goldman, Michael Zollhöfer
Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e. g., by the integration of differentiable rendering into network training.
no code implementations • ICCV 2019 • Sai Bi, Kalyan Sunkavalli, Federico Perazzi, Eli Shechtman, Vladimir Kim, Ravi Ramamoorthi
We present a method to improve the visual realism of low-quality, synthetic images, e. g. OpenGL renderings.
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.
no code implementations • 4 Oct 2019 • Omid Poursaeed, Vladimir G. Kim, Eli Shechtman, Jun Saito, Serge Belongie
We capture these subtle changes by applying an image translation network to refine the mesh rendering, providing an end-to-end model to generate new animations of a character with high visual quality.
1 code implementation • ICCV 2019 • Arnab Ghosh, Richard Zhang, Puneet K. Dokania, Oliver Wang, Alexei A. Efros, Philip H. S. Torr, Eli Shechtman
We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects.
no code implementations • ICCV 2019 • Wenqi Xian, Zhengqi Li, Matthew Fisher, Jonathan Eisenmann, Eli Shechtman, Noah Snavely
We introduce UprightNet, a learning-based approach for estimating 2DoF camera orientation from a single RGB image of an indoor scene.
1 code implementation • 4 Jun 2019 • Ohad Fried, Ayush Tewari, Michael Zollhöfer, Adam Finkelstein, Eli Shechtman, Dan B. Goldman, Kyle Genova, Zeyu Jin, Christian Theobalt, Maneesh Agrawala
To edit a video, the user has to only edit the transcript, and an optimization strategy then chooses segments of the input corpus as base material.
1 code implementation • CVPR 2019 • Chen-Hsuan Lin, Oliver Wang, Bryan C. Russell, Eli Shechtman, Vladimir G. Kim, Matthew Fisher, Simon Lucey
In this paper, we address the problem of 3D object mesh reconstruction from RGB videos.
1 code implementation • CVPR 2019 • Yijun Li, Chen Fang, Aaron Hertzmann, Eli Shechtman, Ming-Hsuan Yang
We propose a high-quality photo-to-pencil translation method with fine-grained control over the drawing style.
1 code implementation • CVPR 2019 • Ning Yu, Connelly Barnes, Eli Shechtman, Sohrab Amirghodsi, Michal Lukac
This paper addresses the problem of interpolating visual textures.
1 code implementation • EMNLP 2018 • Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, Bryan Russell
To benchmark whether our model, and other recent video localization models, can effectively reason about temporal language, we collect the novel TEMPOral reasoning in video and language (TEMPO) dataset.
1 code implementation • ECCV 2018 • Xinchen Yan, Akash Rastogi, Ruben Villegas, Kalyan Sunkavalli, Eli Shechtman, Sunil Hadap, Ersin Yumer, Honglak Lee
Our model jointly learns a feature embedding for motion modes (that the motion sequence can be reconstructed from) and a feature transformation that represents the transition of one motion mode to the next motion mode.
Ranked #8 on
Human Pose Forecasting
on Human3.6M
(ADE metric)
1 code implementation • ECCV 2018 • Wei-Sheng Lai, Jia-Bin Huang, Oliver Wang, Eli Shechtman, Ersin Yumer, Ming-Hsuan Yang
Our method takes the original unprocessed and per-frame processed videos as inputs to produce a temporally consistent video.
12 code implementations • 9 Apr 2018 • Fujun Luan, Sylvain Paris, Eli Shechtman, Kavita Bala
Copying an element from a photo and pasting it into a painting is a challenging task.
Graphics
2 code implementations • CVPR 2018 • Chen-Hsuan Lin, Ersin Yumer, Oliver Wang, Eli Shechtman, Simon Lucey
We address the problem of finding realistic geometric corrections to a foreground object such that it appears natural when composited into a background image.
24 code implementations • CVPR 2018 • Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, Oliver Wang
We systematically evaluate deep features across different architectures and tasks and compare them with classic metrics.
Ranked #19 on
Video Quality Assessment
on MSU FR VQA Database
6 code implementations • CVPR 2018 • Samaneh Azadi, Matthew Fisher, Vladimir Kim, Zhaowen Wang, Eli Shechtman, Trevor Darrell
In this work, we focus on the challenge of taking partial observations of highly-stylized text and generalizing the observations to generate unobserved glyphs in the ornamented typeface.
7 code implementations • NeurIPS 2017 • Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
Our proposed method encourages bijective consistency between the latent encoding and output modes.
1 code implementation • 28 Sep 2017 • Roey Mechrez, Eli Shechtman, Lihi Zelnik-Manor
Recent work has shown impressive success in transferring painterly style to images.
no code implementations • ICCV 2017 • Nicholas Kolkin, Gregory Shakhnarovich, Eli Shechtman
In many computer vision tasks, for example saliency prediction or semantic segmentation, the desired output is a foreground map that predicts pixels where some criteria is satisfied.
2 code implementations • ICCV 2017 • Lisa Anne Hendricks, Oliver Wang, Eli Shechtman, Josef Sivic, Trevor Darrell, Bryan Russell
A key obstacle to training our MCN model is that current video datasets do not include pairs of localized video segments and referring expressions, or text descriptions which uniquely identify a corresponding moment.
2 code implementations • CVPR 2017 • Zhixin Shu, Ersin Yumer, Sunil Hadap, Kalyan Sunkavalli, Eli Shechtman, Dimitris Samaras
Traditional face editing methods often require a number of sophisticated and task specific algorithms to be applied one after the other --- a process that is tedious, fragile, and computationally intensive.
21 code implementations • CVPR 2017 • Fujun Luan, Sylvain Paris, Eli Shechtman, Kavita Bala
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
2 code implementations • ACCV 2017 • Steve Bako, Soheil Darabi, Eli Shechtman, Jue Wang, Kalyan Sunkavalli, Pradeep Sen
In this work, we automatically detect and remove distracting shadows from photographs of documents and other text-based items.
1 code implementation • 7 Dec 2016 • Roey Mechrez, Eli Shechtman, Lihi Zelnik-Manor
Have you ever taken a picture only to find out that an unimportant background object ended up being overly salient?
1 code implementation • CVPR 2017 • Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, Hao Li
Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal.
6 code implementations • CVPR 2017 • Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
1 code implementation • 12 Sep 2016 • Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
Realistic image manipulation is challenging because it requires modifying the image appearance in a user-controlled way, while preserving the realism of the result.
7 code implementations • 19 Jun 2016 • Leon A. Gatys, Matthias Bethge, Aaron Hertzmann, Eli Shechtman
This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.).
no code implementations • 21 Mar 2016 • Liqian Ma, Jue Wang, Eli Shechtman, Kalyan Sunkavalli, Shi-Min Hu
In this work we propose a fully automatic shadow region harmonization approach that improves the appearance compatibility of the de-shadowed region as typically produced by previous methods.
no code implementations • ICCV 2015 • Siying Liu, Tian-Tsong Ng, Kalyan Sunkavalli, Minh N. Do, Eli Shechtman, Nathan Carr
In this work, we investigate the problem of automatically inferring the lattice structure of near-regular textures (NRT) in real-world images.
1 code implementation • ICCV 2015 • Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, Alexei A. Efros
What makes an image appear realistic?
1 code implementation • 12 Jul 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
As font is one of the core design concepts, automatic font identification and similar font suggestion from an image or photo has been on the wish list of many designers.
Ranked #1 on
Font Recognition
on VFR-Wild
no code implementations • CVPR 2015 • Ohad Fried, Eli Shechtman, Dan B. Goldman, Adam Finkelstein
We propose a new computer vision task we call "distractor prediction."
no code implementations • 31 Mar 2015 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We address a challenging fine-grain classification problem: recognizing a font style from an image of text.
no code implementations • 18 Dec 2014 • Zhangyang Wang, Jianchao Yang, Hailin Jin, Eli Shechtman, Aseem Agarwala, Jonathan Brandt, Thomas S. Huang
We present a domain adaption framework to address a domain mismatch between synthetic training and real-world testing data.
no code implementations • CVPR 2014 • Guang Chen, Jianchao Yang, Hailin Jin, Jonathan Brandt, Eli Shechtman, Aseem Agarwala, Tony X. Han
This paper addresses the large-scale visual font recognition (VFR) problem, which aims at automatic identification of the typeface, weight, and slope of the text in an image or photo without any knowledge of content.
Ranked #1 on
Font Recognition
on VFR-447
no code implementations • CVPR 2013 • Dmitry Rudoy, Dan B. Goldman, Eli Shechtman, Lihi Zelnik-Manor
For example, the time each video frame is observed is a fraction of a second, while a still image can be viewed leisurely.
1 code implementation • 16 Apr 2012 • Dmitry Rudoy, Dan B. Goldman, Eli Shechtman, Lihi Zelnik-Manor
In this work we propose a crowdsourced method for acquisition of gaze direction data from a virtually unlimited number of participants, using a robust self-reporting mechanism (see Figure 1).
Social and Information Networks Human-Computer Interaction