Prashanth Chandran
I am a Research Scientist (Machine Learning) at Google AR/VR. I generally enjoy working on creative application at the intersection of computer vision, graphics, and machine learning. Please take a look at some of my publications to know more about my work :)
Previously I was an Associate Research Scientist at Disney Research|Studios and a member of the Facial VFX group.
I completed my PhD from the Computer Graphics Lab at ETH Zurich and Disney Research|Studios, where I was advised by Prof. Markus Gross, and co-supervised by Dr. Derek Bradley. Before my PhD, I completed my Masters in the Department of Electrical Engineering and Information Technology, also at ETH Zurich, where my focus was on machine learning and computer vision. I received my bachelor’s degree in Electronics and Communications Engineering in 2013 from the Madras Institute of Technology and later worked for 3 years at Caterpillar Inc. as an embedded electronics engineer.
Recent Publications (view more)
Neural Facial Deformation Transfer Eurographics 2025 Project Page |
Spline-based Transformers ECCV 2024 (Oral) Project Page |
Learning a Generalized Physical Face Model From Data Siggraph 2024 Project Page |
Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection Computer Graphics Forum 2024 Project Page |
Anatomically Constrained Implicit Face Models CVPR 2024 Project Page |
Artist-Friendly Relightable and Animatable Neural Heads CVPR 2024 Project Page |
Fast Dynamic Facial Wrinkles Eurographics 2024 Project Page |
Stylize My Wrinkles: Bridging the Gap from Simulation to Reality Eurographics 2024 Project Page |
An Implicit Physical Face Model Driven by Expression and Style Siggraph Asia 2023 Project Page |
A Perceptual Shape Loss for Monocular 3D Face Reconstruction Pacific Graphics 2023 Project Page |
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting CVPR 2023 Project Page |
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Neural Facial Deformation Transfer
Spline-based Transformers
Learning a Generalized Physical Face Model From Data
Infinite 3D Landmarks: Improving Continuous 2D Facial Landmark Detection
Anatomically Constrained Implicit Face Models
Artist-Friendly Relightable and Animatable Neural Heads
Fast Dynamic Facial Wrinkles
Stylize My Wrinkles: Bridging the Gap from Simulation to Reality
An Implicit Physical Face Model Driven by Expression and Style
A Perceptual Shape Loss for Monocular 3D Face Reconstruction
ReNeRF: Relightable Neural Radiance Fields with Nearfield Lighting