Smart Animation Tools

Smart Animation Tools

Benjamin Kenwright (Southampton Solent University, UK)
DOI: 10.4018/978-1-5225-2990-3.ch003
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This chapter discusses the inherent limitations in conventional animation techniques and possible solutions through optimisation and machine learning paradigms. For example, going beyond pre–recorded animation libraries towards more intelligent self-learning models. These models present a range of difficulties in real-world solutions, such as, computational cost, flexibility, and most importantly, artistic control. However, as we discuss in this chapter, advancements in massively parallel processing power and hybrid models provides a transitional medium for these solutions (best of both worlds). We review trends and state of the art techniques and their viability in industry. A particular area of active animation is self–driven characters (i.e., agents mimic the real-world through physics-based models). We discuss and debate each techniques practicality in solving and overcoming current and future limitations.
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Why Is Animation Important?

Digital animation technologies are labour intensive and expensive. This is especially true for films and games (e.g., Star–Wars and Avatar) with costs in the millions of dollars per minute. Not to mention, we are on the dawn of virtual reality – with computer generated scenes that are indistinguishable from the real–world. However, animation technologies are a bottleneck which needs to be addressed through research and innovation to develop new technologies and tools. This chapter address these challenging problems with bold approaches to revolutionise traditional techniques. Developing intelligent animation tools based on optimisation models, such as, evolutionary and genetic algorithms (Kenwright, 2014; Kyto et al., 2015), that go beyond pre–recorded and mechanistic methods towards more cognitive self–learning paradigms. This chapter explains the advantages, limitations, and applications of optimisation algorithms for developing novel techniques and tools to reduce the production costs and improve the level of automation while embracing artistic control and realism (preserving creative freedom) (Lin et al., 2015; Thomas et al., 1995).

What Does Optimisation Have to Do With Animation? Why Evolutionary and Genetic Algorithms?

We need to understand that animation is a complex problem with a set of constraints and requirements which optimisation techniques (such as, evolutionary algorithms) are able to find optimal solutions. These evolving algorithms allow us to develop ‘quasi’ intelligent animation models that are able to perform actions or adapt to unforeseen situations in a controlled life–like manner – leading to a new generation of animation tools. For example, even the simple task of creating a walking animation for a human model is a tedious and complex task that requires animators to capture a number of important characteristics, such as, foot support and balance mechanics, not to mention, behavioural and stylistic qualities – which could be generated by combining optimisation techniques with physical–based representations of human skeletons to simulate a controlled walking process (akin to how a child learns to walk) (Kenwright, 2014; Gritz & Hahn, 1997).

This forward-thinking view leads to tools and techniques that take a radical view of animation – from understanding the mechanics of motion to modelling and optimizing complex actions. Solving animations through optimisation has implications on a global scale, not to mention aiding us in understanding how humans learn and develop their motor skills. For instance, how our brain is able to perform ‘subconsciously’ complex open–ended problem beyond anything we can currently achieve algorithmically or mechanistically. While the laws of mechanics are well defined and understood, control algorithms for controlling and learning complex problems, such as, coordinated movement, akin to what we as humans perform without thinking, are still unknown in artificial intelligence.

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