Investigating how computational tools can improve the production process of stop-motion animation

  • Lindsey Howell

Student thesis: Doctoral ThesisDoctor of Engineering (EngD)


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 withtraditional 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 stateof 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 forplasticine (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.
Date of Award27 Feb 2015
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorPeter Hall (Supervisor) & Philip Child (Supervisor)


  • computer vision
  • computer graphics
  • animation
  • stop-motion animation
  • production pipeline

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