Abhimitra (Abhi) Meka
I am a Research Scientist in the Augmented Reality Perception group at Google where I work with Thabo Beeler, Christoph Rhemann and many other exceptional researchers, engineers and artists. My work lies at the intersection of computer graphics, computer vision and machine learning. I am particularly interested in the process of acquiring, understanding and modifying visual appearance of people and objects in images and videos to enable augmented reality.
I was a visiting postdoctoral scholar at Stanford University working with Maneesh Agrawala and Gordon Wetzstein in 2020. Before that I graduated summa-cum-laude with a Doctorate of Engineering from the Graphics, Vision and Video Group (Now Department of Visual Computing and Artifical Intelligence) at the Max Planck Insitute for Informatics, advised by Christian Theobalt. I was awarded the Eurographics PhD Award 2021 Honorable Mention for my doctoral dissertation.
I encourage you to talk to me about inverse rendering, view synthesis and relighting for Augmented Reality applications. Or we could talk if you are familiar with this!
A volumetric representation anchored on a 3D morphable model to generate photorealistic 3D avatars from monocular video
EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes
A volumetric synthesis model for high-quality photorealistic performance capture and animation of human eyes
VoLux-GAN: A Generative Model for 3D Face Synthesis with HDRI Relighting
A generative model that synthesizes novel volumetric 3D human heads that can be photorealistically relit under desired environments
VariTex:Variational Neural Face Textures
A generative model that synthesizes novel 3D human faces with fine-grained explicit control over extreme poses and expressions
Real-time Global Illumination Decomposition of Videos
ACM Transactions on Graphics 2021 (Presented at SIGGRAPH 2021)
An optimization based technique to decompose videos into per-frame reflectance and global illumination layers in real-time
Deep Reflectance Fields: High-Quality Facial Reflectance Field Inference From Color Gradient Illumination
ACM Transactions on Graphics (Proceedings of SIGGRAPH) 2019
A neural rendering technique to capture fully relightable high-resolution dynamic facial performances in a Lightstage
LIME: Live Intrinsic Material Estimation
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018 - Spotlight
An ML technique to estimate high-frequency material of an object of any shape from a single image and lighting from depth+video
Live User-Guided Intrinsic Video For Static Scenes
IEEE Transactions on Visualization and Computer Graphics (TVCG) 2017
Presented at International Symposium on Mixed and Augmented Reality (ISMAR) 2017
An interactive technique guided by 3D user strokes to perform geometry reconstruction and intrinsic decomposition of static scenes