Becoming friends with pixels through intermediate representations

 

Abstract:

Manipulation of natural images for tasks like object insertion, out-painting or creating animations is extremely difficult if we operate purely in the pixel domain. Dr Kuldeep Kulkarni talked about the advantages of manipulating visual data by directly expressing them in intermediate representations and manipulating them instead of the pixels. Specifically, the focus was on his recent works with image out-painting and animating still images as target applications. He first talked about a semantically-aware novel paradigm to perform image extrapolation that enables the addition of new object instances. Expressing the images in semantic label space allows us to complete the existing objects more effectively and add completely new objects that otherwise are very difficult when working in the pixel domain. Dr. Kulkarni also discussed methods he developed to exploit intermediate representations like optical flow and surface normal maps to generate cinema graphs depicting the animation of fluid elements and human clothing.

Bio:

Dr Kuldeep Kulkarni is a research scientist at Adobe Research, Bengaluru, working in the BigData Experience Labs. His current research interests broadly span computer vision, with a bent toward synthesizing beautiful and creative images and clips. Before this, Kuldeep did a post-doc stint at Carnegie Mellon University with Prof. Aswin Sankaranarayanan. Kuldeep received his Ph.D. in Electrical Engineering from Arizona State University under the supervision of Prof. Pavan Turaga. His PhD thesis focussed on tackling computer vision problems from compressive cameras at extremely low measurement rates, combining computer vision and compressed sensing.