Abstract

Within the fields of visual effects and animation, humans have historically spent countless painstaking hours mastering the skill of drawing frame-by-frame animations. One such animation technique that has been widely used in the animation and visual effects industry is called "rotoscoping" and has allowed uniquely stylized animations to capture the motion of real life action sequences, however it is a very complex and time consuming process. Automating this arduous technique would free animators from performing frame by frame stylization and allow them to concentrate on their own artistic contributions. This thesis introduces a new artificial system based on an existing neural style transfer method which creates artistically stylized animations that simultaneously reproduce both the motion of the original videos that they are derived from and the unique style of a given artistic work. This system utilizes a convolutional neural network framework to extract a hierarchy of image features used for generating images that appear visually similar to a given artistic style while at the same time faithfully preserving temporal content. The use of optical flow allows the combination of style and content to be integrated directly with the apparent motion over frames of a video to produce smooth and visually appealing transitions. The implementation described in this thesis demonstrates how biologically-inspired systems such as convolutional neural networks are rapidly approaching human-level behavior in tasks that were once thought impossible for computers. Such a complex task elucidates the current and future technical and artistic capabilities of such biologically-inspired neural systems as their horizons expand exponentially. Further, this research provides unique insights into the way that humans perceive and utilize temporal information in everyday tasks. A secondary implementation that is explored in this thesis seeks to improve existing convolutional neural networks using a biological approach to the way these models adapt to their inputs. This implementation shows how these pattern recognition systems can be greatly improved by integrating recent neuroscience research into already biologically inspired systems. Such a novel hybrid activation function model replicates recent findings in the field of neuroscience and shows significant advantages over existing static activation functions.

Library of Congress Subject Headings

Animation (Cinematography)--Data processing; Neural networks (Computer science); Natural computation

Publication Date

7-2016

Document Type

- Please Select One -

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering (KGCOE)

Advisor

Raymond Ptucha

Advisor/Committee Member

Andreas Savakis

Advisor/Committee Member

Nathan Cahill

Comments

Physical copy available from RIT's Wallace Library at TR897.5 .D87 2016

Additional Files
README.txt (1 kB)
StyleFlowT1.flv (12823 kB)

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RIT – Main Campus

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