I am a senior research scientist and engineer at Adobe Research. My research sits at the intersection of computer vision, computer graphics, and machine learning, and focuses on learning and using priors from natural images. I'm interested in teaching machines to perceive and control geometry, materials, and lighting in service of creative tools. At Adobe, I worked on Stager's match image perspective and lighting technologies, and my research has been integrated into Photoshop's Neural Filters and Adobe Firefly.
I received my Ph.D. from the Computer Vision and Systems Laboratory of Laval University under the supervision of Jean-François Lalonde and Paulo Gotardo. My thesis received the 2018 CIPPRS Doctoral Dissertation Award.
I can be reached by email at holdgeof@ad*be.com.
Snapshot polarimetric
diffuse-specular separation
Guided Co-Modulated GAN for 360° Field of View
Extrapolation
Material Swapping for 3D Scenes using a
Learnt Material Similarity Measure
PhotoScene: Photorealistic
Material and Lighting Transfer for Indoor Scenes
Temporally
Consistent Relighting for Portrait Videos
NeuTex: Neural Texture Mapping for Volumetric Neural
Rendering
Deep Reflectance Volumes: Relightable
Reconstructions from Multi-View Photometric Images
Deep Multi Depth Panoramas for View
Synthesis
Single View Metrology in the Wild
LandscapeAR: Large Scale Outdoor Augmented
Reality by Matching Photographs with Terrain Models Using Learned Descriptors
RGB2AO: Ambient Occlusion
Generation from RGB Images
AutoToon: Automatic Geometric Warping
for Face Cartoon Generation
Single Day Outdoor Photometric Stereo
Deep Parametric Indoor Lighting
Estimation
Deep Sky Modeling for Single Image Outdoor
Lighting Estimation
All-Weather Deep Outdoor Lighting
Estimation
A
Perceptual Measure for Deep Single Image Camera Calibration
Deep Outdoor
Illumination Estimation
x-hour Outdoor
Photometric Stereo
What Is a Good Day for
Outdoor Photometric Stereo?