W
Wojciech Matusik
Researcher at Massachusetts Institute of Technology
Publications - 352
Citations - 20680
Wojciech Matusik is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Computer science & Rendering (computer graphics). The author has an hindex of 68, co-authored 323 publications receiving 17101 citations. Previous affiliations of Wojciech Matusik include Mitsubishi Electric & Disney Research.
Papers
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Proceedings ArticleDOI
Image-based visual hulls
TL;DR: This paper describes an efficient image-based approach to computing and shading visual hulls from silhouette image data that takes advantage of epipolar geometry and incremental computation to achieve a constant rendering cost per rendered pixel.
Proceedings ArticleDOI
A data-driven reflectance model
TL;DR: This work presents a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data that lets users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space.
Journal ArticleDOI
Articulated mesh animation from multi-view silhouettes
TL;DR: This work demonstrates a practical software system for capturing details in mesh animations from multi-view video recordings given a stream of synchronized video images that record a human performance from multiple viewpoints and an articulated template of the performer.
Journal ArticleDOI
Learning the signatures of the human grasp using a scalable tactile glove.
Subramanian Sundaram,Petr Kellnhofer,Yunzhu Li,Jun-Yan Zhu,Antonio Torralba,Wojciech Matusik +5 more
TL;DR: Tactile patterns obtained from a scalable sensor-embedded glove and deep convolutional neural networks help to explain how the human hand can identify and grasp individual objects and estimate their weights.
Posted Content
Eye Tracking for Everyone
Kyle Krafka,Aditya Khosla,Petr Kellnhofer,Harini Kannan,Suchendra M. Bhandarkar,Wojciech Matusik,Antonio Torralba +6 more
TL;DR: iTracker, a convolutional neural network for eye tracking, is trained, which achieves a significant reduction in error over previous approaches while running in real time (10-15fps) on a modern mobile device.