Recent work has shown impressive success in automatically synthesizing new images with desired properties such as transferring painterly style, modifying facial expressions, increasing image resolution or manipulating the center of attention of the image. In this talk I will discuss two of the standing challenges in image synthesis and how we tackle them:
- I will describe our efforts in making the synthesized images more photorealistic.
- I will further show how we can broaden the scope of data that can be used for training synthesis networks, and with that provide a solution to new applications.
Lihi Zelnik-Manor is an Associate Professor in the Faculty of Electrical Engineering in the Technion, Israel. Between 2014-2016 she was a visiting Associate Professor at CornellTech. Prior to the Technion, she worked as a post-doctoral fellow in the Department of Engineering and Applied Science in the California Institute of Technology (Caltech). She holds a PhD and MSc (with honors) in Computer Science from the Weizmann Institute of Science and a BSc (summa cum laude) in Mechanical Engineering from the Technion. Prof. Zelnik-Manor’ awards and honors include the Israeli high-education planning and budgeting committee (Vatat) scholarship for outstanding Ph.D. students, the Sloan-Swartz postdoctoral fellowship, the best Student Paper Award at the IEEE SMI'05, the AIM@SHAPE Best Paper Award 2005 and the Outstanding Reviewer Award at CVPR'08. She is also a recipient of the Gutwirth prize. Prof Zelnik-Manor has served as Area Chair for ECCV and CVPR multiple times, as Program Chair of CVPR’16 and as Associate Editor at TPAMI.