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.


Neural Relighting

2019, Oct 18    

For my guided research project, we present an image-based relighting method that can synthesize scenes under novel lighting using image synthesis models based on deep learning. Our method extends the Deffered Neural Rendering pipeline to perform relighting tasks. This pipeline combines the traditional graphics pipeline with learnable components: neural textures and deferred neural ren- derer. We evaluate extensively the effectiveness of our approach in several experiments, on both synthetic and real scenes. Results prove that Neural Rendering pipeline is able to reproduce complex relighting tasks like modeling high frequency lighting effects such as specularities and shadows.

Results on Light Stage Dataset

Helmet Front Fighting Knight Plant

Advisor: Justus Thies Supervisor: Matthias Niessner

Github repository Document