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Su, Shih-Yang

University of British Columbia

Realistic 3D human models have extensive applications across various domains, including entertainment, healthcare, sports, fashion, and more. The challenges in recreating life-like, high-fidelity virtual humans lie in capturing the subtle, nuanced expressions and intricate, complex body dynamics. Consequently, the human digitalization process often requires sophisticated, tailor-made multi-camera capture studios and high-precision motion-tracking systems, limiting accessibility to only a selected few. While recent developments in deep learning have made modeling virtual characters from videos possible, existing approaches still rely on template meshes and 3D surface priors constructed from accurate 3D scans, labels, and multi-view captures. In this dissertation, we take steps in template-free 3D digitalization, enabling 3D animatable human modeling directly from video footage without 3D annotations and surface priors. Our important contributions include: 1) an analysis-by-synthesis framework for jointly learning 3D body shape, appearance, and pose directly from monocular videos; 2) a disentangled body feature representation without pre-defined 3D surfaces for sample-efficient learning and unseen animation generalization; 3) a memory-efficient factorized volume representation for capturing local appearance and geometry structures; 4) a hybrid human body model combining point-based and neural-fields representations for creating 3D avatars with detailed and consistent appearances. In conclusion, we develop approaches that build upon each other to advance the technologies for accessible human digitalization.

Text

2024-03-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87534

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/