13,902 research outputs found
FML: Face Model Learning from Videos
Monocular image-based 3D reconstruction of faces is a long-standing problem
in computer vision. Since image data is a 2D projection of a 3D face, the
resulting depth ambiguity makes the problem ill-posed. Most existing methods
rely on data-driven priors that are built from limited 3D face scans. In
contrast, we propose multi-frame video-based self-supervised training of a deep
network that (i) learns a face identity model both in shape and appearance
while (ii) jointly learning to reconstruct 3D faces. Our face model is learned
using only corpora of in-the-wild video clips collected from the Internet. This
virtually endless source of training data enables learning of a highly general
3D face model. In order to achieve this, we propose a novel multi-frame
consistency loss that ensures consistent shape and appearance across multiple
frames of a subject's face, thus minimizing depth ambiguity. At test time we
can use an arbitrary number of frames, so that we can perform both monocular as
well as multi-frame reconstruction.Comment: CVPR 2019 (Oral). Video: https://www.youtube.com/watch?v=SG2BwxCw0lQ,
Project Page: https://gvv.mpi-inf.mpg.de/projects/FML19
Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval
Humans use context and scene knowledge to easily localize moving objects in
conditions of complex illumination changes, scene clutter and occlusions. In
this paper, we present a method to leverage human knowledge in the form of
annotated video libraries in a novel search and retrieval based setting to
track objects in unseen video sequences. For every video sequence, a document
that represents motion information is generated. Documents of the unseen video
are queried against the library at multiple scales to find videos with similar
motion characteristics. This provides us with coarse localization of objects in
the unseen video. We further adapt these retrieved object locations to the new
video using an efficient warping scheme. The proposed method is validated on
in-the-wild video surveillance datasets where we outperform state-of-the-art
appearance-based trackers. We also introduce a new challenging dataset with
complex object appearance changes.Comment: Under review with the IEEE Transactions on Circuits and Systems for
Video Technolog
Computer-based tracking, analysis, and visualization of linguistically significant nonmanual events in American Sign Language (ASL)
Our linguistically annotated American Sign Language (ASL) corpora have formed a basis for research to automate detection by
computer of essential linguistic information conveyed through facial expressions and head movements. We have tracked head position
and facial deformations, and used computational learning to discern specific grammatical markings. Our ability to detect, identify, and
temporally localize the occurrence of such markings in ASL videos has recently been improved by incorporation of (1) new techniques
for deformable model-based 3D tracking of head position and facial expressions, which provide significantly better tracking accuracy
and recover quickly from temporary loss of track due to occlusion; and (2) a computational learning approach incorporating 2-level
Conditional Random Fields (CRFs), suited to the multi-scale spatio-temporal characteristics of the data, which analyses not only
low-level appearance characteristics, but also the patterns that enable identification of significant gestural components, such as
periodic head movements and raised or lowered eyebrows. Here we summarize our linguistically motivated computational approach
and the results for detection and recognition of nonmanual grammatical markings; demonstrate our data visualizations, and discuss the
relevance for linguistic research; and describe work underway to enable such visualizations to be produced over large corpora and
shared publicly on the Web
- …