Stephen Bailey

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2021-30

May 1, 2021

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-30.pdf

An important goal of character animation is to create believable, life-like movements and expressions. For film, artists spend significant amounts of effort to add sufficient complexity to a character rig to enable believable and emotionally evocative performances. However, once a complex character rig has been authored, an artist then needs to spend a significant amount of effort to animate a character and bring it to life. For video games, mesh deformations and geometry processing must be real-time, which affects the types of deformations included in a character rig for an interactive application. As a result, video game characters tend to lack some of the sophisticated deformations and motions seen in film-quality characters.

This dissertation explores applications of machine learning for improving the quality of deformations in real-time character rigs as well as applications to assist artists in producing high-quality animations. We detail a deep learning-based approach to enable complex film-quality mesh deformations to run in real-time for both a character's body and face. Our method learns mesh deformations from an existing character rig and produces an accurate approximation using significantly less computational time. In addition to mesh deformations, we present a statistical approach to synthesize novel animations from a collection of artist-created animations. Thus, single-use animations for film can be leveraged for additional applications. We also present a method for generating facial animation from a recorded performance, which provides artists with an initial animation that can be fine-tuned to meet stylistic and expressive needs.

Advisors: James O'Brien

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BibTeX citation:

@phdthesis{Bailey:EECS-2021-30,
    Author= {Bailey, Stephen},
    Title= {Applications of Machine Learning for Character Animation},
    School= {EECS Department, University of California, Berkeley},
    Year= {2021},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-30.html},
    Number= {UCB/EECS-2021-30},
    Abstract= {An important goal of character animation is to create believable, life-like movements and expressions.  For film, artists spend significant amounts of effort to add sufficient complexity to a character rig to enable believable and emotionally evocative performances.  However, once a complex character rig has been authored, an artist then needs to spend a significant amount of effort to animate a character and bring it to life.  For video games, mesh deformations and geometry processing must be real-time, which affects the types of deformations included in a character rig for an interactive application.  As a result, video game characters tend to lack some of the sophisticated deformations and motions seen in film-quality characters.

This dissertation explores applications of machine learning for improving the quality of deformations in real-time character rigs as well as applications to assist artists in producing high-quality animations.  We detail a deep learning-based approach to enable complex film-quality mesh deformations to run in real-time for both a character's body and face.  Our method learns mesh deformations from an existing character rig and produces an accurate approximation using significantly less computational time.  In addition to mesh deformations, we present a statistical approach to synthesize novel animations from a collection of artist-created animations.  Thus, single-use animations for film can be leveraged for additional applications.  We also present a method for generating facial animation from a recorded performance, which provides artists with an initial animation that can be fine-tuned to meet stylistic and expressive needs.},
}

EndNote citation:

%0 Thesis
%A Bailey, Stephen 
%T Applications of Machine Learning for Character Animation
%I EECS Department, University of California, Berkeley
%D 2021
%8 May 1
%@ UCB/EECS-2021-30
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2021/EECS-2021-30.html
%F Bailey:EECS-2021-30