333 citations · 1.2k across the 17 of their papers we have counts for
18 papers
Blended Latent Diffusion
Omri Avrahami, Ohad Fried, Dani Lischinski
The tremendous progress in neural image generation, coupled with the emergence of seemingly omnipotent vision-language models has finally enabled text-based interfaces for creating…
ShapeFormer: Transformer-based Shape Completion via Sparse Representation
Xingguang Yan, Liqiang Lin, Niloy J. Mitra +3
We present ShapeFormer, a transformer-based network that produces a distribution of object completions, conditioned on incomplete, and possibly noisy, point clouds. The resultant d…
Classification-Regression for Chart Comprehension
Matan Levy, Rami Ben-Ari, Dani Lischinski
Chart question answering (CQA) is a task used for assessing chart comprehension, which is fundamentally different from understanding natural images. CQA requires analyzing the rela…
ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
Jinming Cao, Hanchao Leng, Dani Lischinski +3
RGB-D semantic segmentation has attracted increasing attention over the past few years. Existing methods mostly employ homogeneous convolution operators to consume the RGB and dept…
Evaluation and Comparison of Edge-Preserving Filters
Sarah Gingichashvili, Dani Lischinski
Edge-preserving filters play an essential role in some of the most basic tasks of computational photography, such as abstraction, tonemapping, detail enhancement and texture remova…
Palettailor: Discriminable Colorization for Categorical Data
Kecheng Lu, Mi Feng, Xin Chen +5
We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods…
Unpaired Motion Style Transfer from Video to Animation
Kfir Aberman, Yijia Weng, Dani Lischinski +2
Transferring the motion style from one animation clip to another, while preserving the motion content of the latter, has been a long-standing problem in character animation. Most e…
Skeleton-Aware Networks for Deep Motion Retargeting
Kfir Aberman, Peizhuo Li, Dani Lischinski +3
We introduce a novel deep learning framework for data-driven motion retargeting between skeletons, which may have different structure, yet corresponding to homeomorphic graphs. Imp…
Cross-Domain Cascaded Deep Feature Translation
Oren Katzir, Dani Lischinski, Daniel Cohen-Or
In recent years we have witnessed tremendous progress in unpaired image-to-image translation methods, propelled by the emergence of DNNs and adversarial training strategies. Howeve…
Learning Character-Agnostic Motion for Motion Retargeting in 2D
Kfir Aberman, Rundi Wu, Dani Lischinski +2
Analyzing human motion is a challenging task with a wide variety of applications in computer vision and in graphics. One such application, of particular importance in computer anim…
Deep Video-Based Performance Cloning
Kfir Aberman, Mingyi Shi, Jing Liao +3
We present a new video-based performance cloning technique. After training a deep generative network using a reference video capturing the appearance and dynamics of a target actor…
SAGNet:Structure-aware Generative Network for 3D-Shape Modeling
Zhijie Wu, Xiang Wang, Di Lin +3
We present SAGNet, a structure-aware generative model for 3D shapes. Given a set of segmented objects of a certain class, the geometry of their parts and the pairwise relationships…
DiDA: Disentangled Synthesis for Domain Adaptation
Jinming Cao, Oren Katzir, Peng Jiang +4
Unsupervised domain adaptation aims at learning a shared model for two related, but not identical, domains by leveraging supervision from a source domain to an unsupervised target…
Non-Stationary Texture Synthesis by Adversarial Expansion
Yang Zhou, Zhen Zhu, Xiang Bai +3
The real world exhibits an abundance of non-stationary textures. Examples include textures with large-scale structures, as well as spatially variant and inhomogeneous textures. Whi…
Neural Best-Buddies: Sparse Cross-Domain Correspondence
Kfir Aberman, Jing Liao, Mingyi Shi +3
Correspondence between images is a fundamental problem in computer vision, with a variety of graphics applications. This paper presents a novel method for sparse cross-domain corre…
Neuron-level Selective Context Aggregation for Scene Segmentation
Zhenhua Wang, Fanglin Gu, Dani Lischinski +3
Contextual information provides important cues for disambiguating visually similar pixels in scene segmentation. In this paper, we introduce a neuron-level Selective Context Aggreg…
A Holistic Approach for Data-Driven Object Cutout
Huayong Xu, Yangyan Li, Wenzheng Chen +3
Object cutout is a fundamental operation for image editing and manipulation, yet it is extremely challenging to automate it in real-world images, which typically contain considerab…
Printed Perforated Lampshades for Continuous Projective Images
Haisen Zhao, Lin Lu, Yuan Wei +4
We present a technique for designing 3D-printed perforated lampshades, which project continuous grayscale images onto the surrounding walls. Given the geometry of the lampshade and…