AI-Assisted Pipeline for 3D Face Avatar Generation

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dc.contributor.advisorNiko Troje
dc.contributor.authorAmin Fadaeinejad
dc.date.accessioned2024-11-07T11:14:37Z
dc.date.available2024-11-07T11:14:37Z
dc.date.copyright2024-09-04
dc.date.issued2024-11-07
dc.date.updated2024-11-07T11:14:36Z
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster's
dc.degree.nameMASc - Master of Applied Science
dc.description.abstractFilling virtual environments with realistic-looking avatars is essential for games, film production, and virtual reality. Creating a fun and engaging experience requires a wide variety of different-looking avatars. There are two main methods to create realistic-looking avatars. One is to scan a real person's face using a light room. The second is for the artist/designer to create the avatar manually using advanced tools. Both of these approaches are expensive in terms of time, computing, and human labour. This thesis leverages generative models like Generative Adversarial Networks (GANs) and Variational Auto-Encoders (VAEs) to automate avatar creation. Our pipeline offers control over three aspects: face shape, skin color, and fine details like beards or wrinkles. This provides artists flexibility in avatar creation and can integrate with tools like MOSAR for controlling avatars from 2D images.
dc.identifier.urihttps://hdl.handle.net/10315/42489
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subjectComputer science
dc.subject.keywordsComputer Vision
dc.subject.keywordsComputer Graphics
dc.subject.keywordsComputer Science
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsMachine Learning
dc.subject.keywordsDeep Learning
dc.subject.keywordsGenAI
dc.subject.keywordsGenerative Models
dc.subject.keywordsGame Industry
dc.titleAI-Assisted Pipeline for 3D Face Avatar Generation
dc.typeElectronic Thesis or Dissertation

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