Date of Award
2025-12-01
Degree Name
Master of Science
Department
Computer Engineering
Advisor(s)
Rodrigo Romero
Abstract
Applications of 3D face reconstruction include biometric authentication, personalized avatars and digital identity, medical visualization, forensic analysis, and broader human-computer interaction. We propose an approach to 3D face reconstruction that can generate a fully textured 3D facial model using only two grayscale images: a front view and a profile view of the subject. Once trained, the system can perform the reconstruction autonomously without manual intervention. Unlike traditional methods requiring multi-camera setups, depth sensors, or cloud-based processing, the proposed approach runs fully offline on a standard CPU, supporting dynamic execution across CPU cores and eliminating the need for a dedicated GPU. A Conditional Deep Generative Adversarial Network (CDGAN) merges identity features from both input images, and the final 3D output is exported in OBJ/MTL format. The method reduces average model size by approximately 70%, enabling faster rendering and easier storage on devices with limited resources. This demonstrates that high-quality 3D face reconstruction can be achieved from minimal input and autonomous CPU-based neural processing, providing a foundation for secure, accessible, and privacy-preserving on-device 3D vision technology.
Language
en
Provenance
Received from ProQuest
Copyright Date
2025-12
File Size
124 p.
File Format
application/pdf
Rights Holder
Mario Alberto De La Cruz Armendariz
Recommended Citation
De La Cruz Armendariz, Mario Alberto, "3D Face Modeling From 2D Images Using Deep Neural Networks" (2025). Open Access Theses & Dissertations. 4537.
https://scholarworks.utep.edu/open_etd/4537