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Media type:
E-Article
Title:
Decaf: Monocular Deformation Capture for Face and Hand Interactions
Contributor:
Shimada, Soshi;
Golyanik, Vladislav;
Pérez, Patrick;
Theobalt, Christian
Published:
Association for Computing Machinery (ACM), 2023
Published in:
ACM Transactions on Graphics, 42 (2023) 6, Seite 1-16
Language:
English
DOI:
10.1145/3618329
ISSN:
0730-0301;
1557-7368
Origination:
Footnote:
Description:
Existing methods for 3D tracking from monocular RGB videos predominantly consider articulated and rigid objects ( e.g. , two hands or humans interacting with rigid environments). Modelling dense non-rigid object deformations in this setting ( e.g. when hands are interacting with a face), remained largely unaddressed so far, although such effects can improve the realism of the downstream applications such as AR/VR, 3D virtual avatar communications, and character animations. This is due to the severe ill-posedness of the monocular view setting and the associated challenges ( e.g. , in acquiring a dataset for training and evaluation or obtaining the reasonable non-uniform stiffness of the deformable object). While it is possible to naïvely track multiple non-rigid objects independently using 3D templates or parametric 3D models, such an approach would suffer from multiple artefacts in the resulting 3D estimates such as depth ambiguity, unnatural intra-object collisions and missing or implausible deformations. Hence, this paper introduces the first method that addresses the fundamental challenges depicted above and that allows tracking human hands interacting with human faces in 3D from single monocular RGB videos. We model hands as articulated objects inducing non-rigid face deformations during an active interaction. Our method relies on a new hand-face motion and interaction capture dataset with realistic face deformations acquired with a markerless multi-view camera system. As a pivotal step in its creation, we process the reconstructed raw 3D shapes with position-based dynamics and an approach for non-uniform stiffness estimation of the head tissues, which results in plausible annotations of the surface deformations, hand-face contact regions and head-hand positions. At the core of our neural approach are a variational auto-encoder supplying the hand-face depth prior and modules that guide the 3D tracking by estimating the contacts and the deformations. Our final 3D hand and face reconstructions are realistic and more plausible compared to several baselines applicable in our setting, both quantitatively and qualitatively. https://vcai.mpi-inf.mpg.de/projects/Decaf