activity
20182022
most citedPredictive and Generative Neural Networks for Object Functionality

13 citations · 29 across the 11 of their papers we have counts for

collaborators

18 papers

cs.CV2022

Prediction of Scene Plausibility

Or Nachmias, Ohad Fried, Ariel Shamir

Understanding the 3D world from 2D images involves more than detection and segmentation of the objects within the scene. It also includes the interpretation of the structure and ar…

cs.CV2022

Neural Font Rendering

Daniel Anderson, Ariel Shamir, Ohad Fried

Recent advances in deep learning techniques and applications have revolutionized artistic creation and manipulation in many domains (text, images, music); however, fonts have not y…

cs.CV20222 cited

DeepPortraitDrawing: Generating Human Body Images from Freehand Sketches

Xian Wu, Chen Wang, Hongbo Fu +3

Researchers have explored various ways to generate realistic images from freehand sketches, e.g., for objects and human faces. However, how to generate realistic human body images…

cs.CV2022

Semantic Segmentation in Art Paintings

Nadav Cohen, Yael Newman, Ariel Shamir

Semantic segmentation is a difficult task even when trained in a supervised manner on photographs. In this paper, we tackle the problem of semantic segmentation of artistic paintin…

cs.CV20221 cited

How Low Can We Go? Pixel Annotation for Semantic Segmentation

Daniel Kigli, Ariel Shamir, Shai Avidan

How many labeled pixels are needed to segment an image, without any prior knowledge? We conduct an experiment to answer this question. In our experiment, an Oracle is using Active…

cs.GR202211 cited

CLIPasso: Semantically-Aware Object Sketching

Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo +5

Abstraction is at the heart of sketching due to the simple and minimal nature of line drawings. Abstraction entails identifying the essential visual properties of an object or scen…