|Date:||June 28, 2019 (Friday)|
|Time:||11:00 am - 12:00 pm|
|Venue:||Room 121, 1/F, Ho Sin-Hang Engineering Building, The Chinese University of Hong Kong, Shatin, N.T.|
|Speaker:|| Prof. Daniel COHEN-OR
An interesting question is whether a machine can assist humans in being creative and inspire a user during the creation of 3D models or a shape in general. One possible means to achieve this is through a design gallery which presents a variety of computed suggestive designs from which the user can pick the ones he likes the best. The ensuing challenge is how to come up with intriguing suggestions which inspire creativity, rather than banal suggestions which stall the design process. In my talk I will discuss about the notion of creative modeling, synthesis of inspiring examples, the analysis of a set, and show a number of recent works that uses Deep Neural Networks that baby step towards this end.
Daniel Cohen-Or is a professor in the School of Computer Science. He received his B.Sc. cum laude in both mathematics and computer science (1985), and M.Sc. cum laude in computer science (1986) from Ben-Gurion University, and Ph.D. from the Department of Computer Science (1991) at State University of New York at Stony Brook. He received the 2005 Eurographics Outstanding Technical Contributions Award. He was sitting on the editorial board of a number of international journals, and a member of many the program committees of several international conferences. He was the recipient of the Eurographics Outstanding Technical Contributions Award in 2005, ACM SIGGRAPH Computer Graphics Achievement Award in 2018.
In 2013 he received The People’s Republic of China Friendship Award. In 2015 he has been named a Thomson Reuters Highly Cited Researcher. In 2019 he won The Kadar Family Award for Outstanding Research. His research interests are in computer graphics, in particular, synthesis, processing and modeling techniques.
Enquiries: Ms. Shirley Lau at tel. 3943 8439
For more information, please refer to http://www.cse.cuhk.edu.hk/en/events