Events
PhD DefensePhysics-based Neural Deformable Models, applications to computer vision, graphics and beyond |
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Wednesday, August 27, 2025, 01:30pm - 03:00pm |
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Speaker: Di Liu
Bio
Location : CBIM 22
Committee:
Professor Dimitris Metaxas
Professor Jie Gao
Professor Konstantinos Michmizos
Profesor Sharon Xiaolei Huang (external)
Event Type: PhD Defense
Abstract: This thesis introduces a novel class of physics-inspired neural networks—Physics-based Neural Deformable Models(PNDMs)—that integrate traditional physics-based deformable models with modern deep learning to achieve interpretable and flexible 3D shape representations. While classical deformable models offer semantic clarity through parametric primitives, they suffer from limited geometric flexibility and dependence on handcrafted initializations. In contrast, PNDMs overcome these limitations by learning parameter functions that generalize primitive geometry, employing diffeomorphic mappings to preserve topology, and leveraging external forces for robust training.We further extend this paradigm in DeFormer, a transformer-based framework that hierarchically disentangles global and local shape deformations, and in LEPARD, which enables 3D articulated part discovery directly from 2D supervision. Finally, we demonstrate the application of our methods in photorealistic avatar reconstruction, including the LUCAS system for layered codec avatars. Together, these contributions bridge interpretable physics-based modeling with scalable neural architectures for shape abstraction, segmentation, registration, and video generation.
Organization:
Rutgers University
School of Arts and Sciences
Department of Computer Science
David Reese Professor College of Information Sciences and Technology Penn State University, University Park
Contact Professor Dimitris Metaxas (Chair)
Zoom Link: https://rutgers.zoom.us/j/91065433139?pwd=oGMiWFEJGqpCj6SyxfIbSnM6wGvjQw.1