This record is in review state, the data has not yet been validated.
This is not the latest version of this item. The latest version can be found here.
Advancing Digital Humans Toward Personalized Human-Centric AI
Loading...
Author / Producer
Date
2026
Publication Type
Doctoral Thesis
ETH Bibliography
yes
Citations
Altmetric
Data
Rights / License
Abstract
Digital human technologies have achieved impressive realism in film and interactive media. However, high-quality results still depend on specialized equipment and expert workflows, which limit their deployment outside studio and laboratory environments. In parallel, recent deep learning methods offer a path toward scalable creation and understanding of digital humans, yet, they often struggle to accommodate the biological variability of real people. This limitation is exacerbated by the scarcity of paired 3D human data, which restricts both generalization and robust adaptation. This thesis addresses this gap by proposing the paradigm of Personalized Human-Centric AI. The central hypothesis posits that explicitly integrating subject-specific biological information into learning-based frameworks can constrain the solution space, thereby mitigating ill-posed problems and reducing reliance on massive training data.
To validate this hypothesis, we conduct studies across three foundational domains of digital human technology: digitalization, modeling, and perception. In the domain of digitalization, we present a pipeline that reconstructs a full-body textured mesh from a single image. By conditioning generative models and implicit neural representations on a personalized body mesh as a geometric anchor, our method completes unseen appearances and recovers high-frequency details that remain coherent with the subject’s body shape. In modeling, we introduce a representation that combines the structure of a personalized template with the expressiveness of learned implicit fields. This approach enables controlled local editing and the transfer of appearance details across subjects while maintaining 3D consistency under articulation. In perception, we propose a body fitting framework to enhance monocular 3D pose estimation. By decoupling shape calibration from pose estimation and utilizing a shape-conditioned generative prior to guide the fitting process, our method achieves more accurate poses that better respect personal anatomical constraints.
Collectively, these contributions demonstrate that explicit personalization is a practical and effective mechanism for advancing high-fidelity digital humans toward human-centric AI. Looking ahead, we envision the future of this field lies in the inter-pollination of digitalization, modeling, and perception to empower physical AI agents capable of reasoning about humans within the 3D world.
Permanent link
Publication status
External links
Editor
Contributors
Examiner : Marc, Pollefeys
Examiner : Siyu, Tang
Examiner : Ming-Hsuan, Yang
Examiner : Lingjie, Liu
Book title
Journal / series
Volume
Pages / Article No.
Publisher
Event
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
03766 - Pollefeys, Marc / Pollefeys, Marc