Juan Raúl Padrón Griffe

Epale! I am PhD candidate at the University of Zaragoza as a member of the PRIME Network with the Graphics and Imaging Lab. My PhD thesis focus on developing theory and methods for efficient rendering of volumetric heterogeneous appearances and it is being supervised by Prof. Adrian Jarabo. Previously, I obtained my Bachelor degree in Computer Science (2015) at the Central University of Venezuela. Later, I received my Master degree in Informatics (2020) at the Technical University of Munich. During my Master studies I focused mostly on the Computer Graphics and Vision subjects, where I was fortunate enough to be advised by Prof. Matthias Niessner and Dr. Justus Thies at the Visual Computing lab to research on 3D Scanning and Neural Rendering.

I am really enthusiastic about the intersection of realistic image synthesis, graphics-based vision and machine learning for the digital acquisition, representation and understanding of the visual world. In particular, I am interested on pushing the state of the art on physically-based rendering, neural rendering and inverse rendering. I also find really interesting other research areas from the Graphics and Imaging Lab such as Human Perception and Transient Imaging.

Projects

Face Relighting In The Wild

Given an arbitrary portrait image and a target lighting as inputs, the algorithm generates the relight version of the portrait image under the target lighting conditions.

2 minute read

Neural Relighting

Extension of the Deferred Neural Rendering pipeline to perform relighting tasks. This is a new paradigm for image synthesis that combines the traditional graphics pipeline with learnable Neural Textures.

1 minute read

RGB-D Reconstruction for Mixed Reality

Real-time mixed reality game using marker-less tracking and 3D reconstruction based on Kinect Fusion pipeline and several Unity modules.

1 minute read

Game Capture

C++ library for the extraction of ground truth labels and the internal game states of video games to train computer vision models for autonomous driving applications.

2 minute read