Stop-motion animation is a traditional form of animation that has been practised for over 100 years. While the unique look and feel of stop-motion animation has been retained in modern productions, the production process has been modernised to take advantage of technological advancements. Modern stop-frame animation production integrates digital imaging technology and computational methods with traditional hand-crafted skills. This portfolio documents three projects undertaken at Aardman Animations, each investigated with the aim of improving efficiency in the stop-motion production process: - Rig removal is the removal of equipment, or ‘rigging’, used on set during stop-motion animation to hold characters or objects in unstable positions. All rigging captured in frames must be removed in post-production and currently manual methods are used which can be very time-consuming. The key task is to separate the character from the rig. In Chapter 2, I present a novel spatio-temporal segmentation algorithm for segmenting characters from stop-motion footage. The algorithm has been designed to work with stop-motion animated content, in contrast to other state of the art algorithms which struggled when tested on stop-motion footage. - Set shift is a problem which occurs when background items on set move subtly over the time taken to shoot a scene. For example, temperature and humidity changes can cause wood to warp during a weekend, changing the position of a background object the following week. These small ‘shifts’ are recorded in the footage and must be corrected in post-production. Chapter 3 describes the problem in detail, investigates potential solutions and explains why solving set shift automatically is a significant challenge. - Plasticine shading is required when a plasticine model has to be generated computationally. One motivation for producing footage computationally is that problems such as rig removal and set shift do not arise. In order to simulate plasticine accurately, the distinct reflectance model of this material must be known and reproduced. By collecting experimental data from plasticine samples and fitting parametric models, I have developed a bespoke surface shading model for plasticine (Chapter 4). This new model provides the best fit to the measured data when compared to existing state of the art surface shaders. It has been implemented into commercially used production systems, for use with existing rendering software. Advancing state of the art research is only one of the challenges when working in a production studio such as Aardman Animations. Additionally, findings must be integrated into the production pipeline. Chapter 5 discusses the challenges and constraints faced when conducting research in this environment. In order for stop-motion animation to remain competitive it is vital that production companies stay up-to-date with technological advancements in research areas that can contribute to their production processes. I conclude by discussing whether technological advancements can help Aardman Animations in improving the efficiency of their stop-motion production pipeline.

This thesis presents a study on how to create a production pipeline using a markerless motion capture system for the creation of animations in computer games. The questions the authors desire to answer are: Is it possible to create a pipeline that uses markerless motion capture for the creation of animations in computer games? And also: Can a markerless motion capture system fit in an animation pipeline for games? This thesis is based on previous work by Kakee Lau (Lau, 2012), a former student of Gotland University College. He describes a pipeline for working with passive optical motion capture for games. To fit the markerless motion capture system, there must be some changes to Lau’s already established pipeline. The method used in this thesis is based on a pipeline described in Lau’s thesis (Lau, 2012). The authors have made some alterations to this pipeline for it to be more suitable for markerless motion capture. The pipeline that the authors propose covers the setup of two Kinect cameras, the calibration, the recording, the cleaning and the preparation for MotionBuilder. Due to some factors that were not taken into consideration during testing, there cannot be any quantitative conclusion in this thesis to which system is the better one. Based on the findings of this study the authors can conclude that a markerless motion capture system is a viable method for game animation creation, yet not giving the same quality of results as a passive optical motion capture system.