T
Thabo Beeler
Researcher at Disney Research
Publications - 80
Citations - 3440
Thabo Beeler is an academic researcher from Disney Research. The author has contributed to research in topics: Motion capture & Computer facial animation. The author has an hindex of 23, co-authored 76 publications receiving 2509 citations. Previous affiliations of Thabo Beeler include Google & The Walt Disney Company.
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Journal ArticleDOI
High-quality single-shot capture of facial geometry
TL;DR: A passive stereo system for capturing the 3D geometry of a face in a single-shot under standard light sources is described, modified of standard stereo refinement methods to capture pore-scale geometry, using a qualitative approach that produces visually realistic results.
Proceedings ArticleDOI
High-quality passive facial performance capture using anchor frames
Thabo Beeler,Fabian Hahn,Derek Bradley,Bernd Bickel,Paul Beardsley,Craig Gotsman,Robert W. Sumner,Markus Gross +7 more
TL;DR: A robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching is introduced, in contrast to previous sequential methods.
High-quality single-shot capture of facial geometry
TL;DR: A passive stereo system for capturing the 3D geometry of a face in a single-shot under standard light sources is described, modified of standard stereo refinement methods to capture pore-scale geometry, using a qualitative approach that produces visually realistic results.
Journal ArticleDOI
State of the Art on Monocular 3D Face Reconstruction, Tracking, and Applications
Michael Zollhöfer,Michael Zollhöfer,Justus Thies,Pablo Garrido,Derek Bradley,Thabo Beeler,Patrick Pérez,Marc Stamminger,Matthias Nießner,Christian Theobalt +9 more
TL;DR: The computer graphics and vision communities have dedicated long standing efforts in building computerized tools for reconstructing, tracking, and analyzing human faces based on visual input to novel and powerful algorithms that obtain impressive results even in the very challenging case of reconstruction from a single RGB or RGB‐D camera.
Journal ArticleDOI
Real-time high-fidelity facial performance capture
TL;DR: This work proposes an automatic way to detect and align the local patches required to train the regressors and run them efficiently in real-time, resulting in high-fidelity facial performance reconstruction with person-specific wrinkle details from a monocular video camera inreal-time.