Neural Relighting - Juan Raul Padron Griffe

Epale! I am a Marie Sklodowska-Curie fellow of the EU Project PRIME and a PhD candidate at the Graphics and Imaging Lab (Universidad de Zaragoza). My PhD thesis under the supervision of Prof. Adolfo Muñoz and Adrian Jarabo focuses on developing theory and methods for accurate and efficient rendering of complex volumetric appearances. 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 conduct my research on 3D Scanning and Neural Rendering.

I am a computer scientist 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. I am currently interested in pushing the state of the art on physically-based rendering of complex multi-scale materials like biological tissues. In my research, I rely on powerful tools like Monte Carlo simulation and gradient-based optimization. In the long term, I believe the combination of powerful forward models (simulation algorithms) and inverse models (gradient-based models) could be impactful in other interesting domains too like computational biology.

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