Building Personalized and Efficient 3D Models

Creating 3D models from casual camera captures enables various creative and cognitive applications for AR/VR, Computational Photography, Robotics, and Medical Imaging. Nonetheless, the underlying 'engines' propelling these 3D models—comprising the hardware systems for capture and machine learning models—are often expensive to build, thereby constraining widespread accessibility. In this talk, I will discuss my group’s effort to develop efficient and accessible 3D models. The first part of the talk will focus on developing lightweight and personalized 3D generative models for facial reconstruction and relighting. The last part of the talk will focus on developing efficient 3D reconstruction algorithms that combine both camera and lighting variations for objects, scenes, and endoscopy images.