3D Head Model Estimation from a Single Photo

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dc.contributor.author Zatserkovnyi, Rostyslav
dc.date.accessioned 2021-06-30T10:12:27Z
dc.date.available 2021-06-30T10:12:27Z
dc.date.issued 2021
dc.identifier.citation Zatserkovnyi, Rostyslav. 3D Head Model Estimation from a Single Photo / Rostyslav Zatserkovnyi; Supervisor: Orest Kupyn; Ukrainian Catholic University, Department of Computer Sciences. – Lviv : [s.n.], 2021. – 35 p.: ill. uk
dc.identifier.uri https://er.ucu.edu.ua/handle/1/2710
dc.description.abstract Today, 3D human head models are widely used in fields such as computer vision, entertainment, healthcare, and biometrics. Since a high-quality scan of a human head is expensive and time-consuming to obtain, machine learning algorithms are used to estimate the shape and texture of a 3D model from a single "in-the-wild" photograph, often taken at extreme angles or with non-uniform illumination. However, as a full head texture cannot be trivially inferred from a single photograph due to self-occlusion, many only focus on modeling an incomplete and partially textured model of the human head. This work proposes a machine learning pipeline that reconstructs a fully textured 3D head model from a single photograph. We collect a novel dataset of 99.3 thousand high-resolution human head textures created from synthetic celebrity photographs. To the best of our knowledge, this is the first UV texture dataset of a similar scale and fidelity. Using this dataset, we train a free-form inpainting GAN that learns to recreate full head textures from partially obscured projections of the input photograph. uk
dc.language.iso en uk
dc.subject 3D human head models uk
dc.subject machine learning pipeline uk
dc.title 3D Head Model Estimation from a Single Photo uk
dc.type Preprint uk
dc.status Публікується вперше uk

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