17,986 research outputs found
Generalizing Gaze Estimation with Weak-Supervision from Synthetic Views
Developing gaze estimation models that generalize well to unseen domains and
in-the-wild conditions remains a challenge with no known best solution. This is
mostly due to the difficulty of acquiring ground truth data that cover the
distribution of possible faces, head poses and environmental conditions that
exist in the real world. In this work, we propose to train general gaze
estimation models based on 3D geometry-aware gaze pseudo-annotations which we
extract from arbitrary unlabelled face images, which are abundantly available
in the internet. Additionally, we leverage the observation that head, body and
hand pose estimation benefit from revising them as dense 3D coordinate
prediction, and similarly express gaze estimation as regression of dense 3D eye
meshes. We overcome the absence of compatible ground truth by fitting rigid 3D
eyeballs on existing gaze datasets and design a multi-view supervision
framework to balance the effect of pseudo-labels during training. We test our
method in the task of gaze generalization, in which we demonstrate improvement
of up to compared to state-of-the-art when no ground truth data are
available, and up to when they are. The project material will become
available for research purposes.Comment: 13 pages, 12 figure
Hybrid One-Shot 3D Hand Pose Estimation by Exploiting Uncertainties
Model-based approaches to 3D hand tracking have been shown to perform well in
a wide range of scenarios. However, they require initialisation and cannot
recover easily from tracking failures that occur due to fast hand motions.
Data-driven approaches, on the other hand, can quickly deliver a solution, but
the results often suffer from lower accuracy or missing anatomical validity
compared to those obtained from model-based approaches. In this work we propose
a hybrid approach for hand pose estimation from a single depth image. First, a
learned regressor is employed to deliver multiple initial hypotheses for the 3D
position of each hand joint. Subsequently, the kinematic parameters of a 3D
hand model are found by deliberately exploiting the inherent uncertainty of the
inferred joint proposals. This way, the method provides anatomically valid and
accurate solutions without requiring manual initialisation or suffering from
track losses. Quantitative results on several standard datasets demonstrate
that the proposed method outperforms state-of-the-art representatives of the
model-based, data-driven and hybrid paradigms.Comment: BMVC 2015 (oral); see also
http://lrs.icg.tugraz.at/research/hybridhape
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