Ohad Fried's Pre FPO will take place on Thursday, July 14, 2016 (today) at 2pm in CS 402. The members of his committee are as follows: Adam Finkelstein (adviser)' Readers: Eli Shechtman (Adobe), Thomas Funkhouser; Non readers: Szymon Rusinkiewicz, David Dobkin. Everyone is invited to attend his talk. The talk title and abstract follow below: Title: "Photo Manipulation (The Easy Way)" Outline: With the growing number of images taken every second, and the growing size of personal photo collections, the need arises for more sophisticated yet easy to use photo manipulation techniques. This talk will present several projects that aim to “democratize” image manipulation: allowing sophisticated edits that are accessible to novices. I will start by presenting a novel way to arrange object collections, such as personal photo libraries. Collections are often presented visually in a grid because it is a compact representation that lends itself well for search and exploration. Most grid layouts are sorted using very basic criteria, such as date or filename. We present a method to arrange collections of objects respecting an arbitrary distance measure. Pairwise distances are preserved as much as possible, while still producing the specific target arrangement which may be a 2D grid, the surface of a sphere, a hierarchy, or any other shape. We show that our method can be used for infographics, collection exploration and data visualization. We present a fast algorithm that can work on large collections and quantitatively evaluate how well distances are preserved. Next, I will show a method to remove unwanted distracting element from images. We propose a new computer vision task — “distractor prediction”. Distractors are the regions of an image that draw attention away from the main subjects and reduce the overall image quality. Removing distractors — for example, using in-painting — can improve the composition of an image. We created two datasets of images with user annotations to identify the characteristics of distractors. We use these datasets to train an algorithm to predict distractor maps. Finally, we use our predictor to automatically enhance images. Lastly, I will show a method to modify the apparent relative pose and distance between camera and subject given a single portrait photo. Our approach fits a full perspective camera and a parametric 3D head model to the portrait, and then builds a 2D warp in the image plane to approximate the effect of a desired change in 3D. We show that this model is capable of correcting objectionable artifacts such as the large noses sometimes seen in “selfies,” or to deliberately bring a distant camera closer to the subject. This framework can also be used to re-pose the subject, as well as to create stereo pairs from an input portrait. We show convincing results on both an existing dataset as well as a new dataset we captured to validate our method.