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 $30\%$ compared to state-of-the-art when no ground truth data are available, and up to $10\%$ 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