Abstract Recently, the progress of learning-by-synthesis has proposed a training model for synthetic images, which can effectively reduce the cost of human and material resources. However, due to the different distribution of synthetic images compared to real images, the desired performance cannot still be achieved. Real images consist of multiple forms of light orientation, while synthetic images consist of a uniform light orientation. These features are considered to be characteristic of outdoor and indoor scenes, respectively. To solve this problem, the previous method learned a model to improve the realism of the synthetic image. Different from the previous methods, this paper takes the first step to purify real images. Through the style transfer task, the distribution of outdoor real images is converted into indoor synthetic images, thereby reducing the influence of light. Therefore, this paper proposes a real-time style transfer network that preserves image content information (e.g., gaze direction, pupil center position) of an input image (real image) while inferring style information (e.g., image color structure, semantic features) of style image (synthetic image). In addition, the network accelerates the convergence speed of the model and adapts to multi-scale images. Experiments were performed using mixed studies (qualitative and quantitative) methods to demonstrate the possibility of purifying real images in complex directions. Qualitatively, it compares the proposed method with the available methods in a series of indoor and outdoor scenarios of the LPW dataset. In quantitative terms, it evaluates the purified image by training a gaze estimation model on the cross data set. The results show a significant improvement over the baseline method compared to the raw real image.
Learning-based methods perform well in appearance-based gaze estimation but require large amounts of training data. Learning-by-synthesis approaches were proposed to solve this problem. @cite_11 presented a novel method to synthesize large amounts of variable eye region images as training data, which addressed the limitation of learning-by-synthesis with respect to the appearance variability and the head pose and gaze angle distribution. Other works learn a feature representation in feature space. For instance, @cite_23 proposed an appearance-based gaze estimation method by supervised adaptive feature extraction @cite_4 @cite_13 @cite_17 @cite_2 @cite_12 and hierarchical mapping model @cite_27 @cite_3 @cite_31 , during which appearance synthesis method is proposed to increase the sample density. @cite_20 introduced a CNN-based gaze estimation method, which concatenated head pose vector in the hidden layer of neural network. This change improved the performance of CNN-based gaze estimation training by synthetic image dataset. @cite_18 presented a learning-by-synthesis approach for appearance-based gaze estimation and trained a 3D gaze estimator by a large amount of cross-subject training data. In their experiments, @math -nearest neighbor was selected as comparison, from which we can see that @math -NN regression estimators can perform well with a large amount of dense training samples.
@cite_18 @cite_11 @cite_4 @cite_3 @cite_27 @cite_23 @cite_2 @cite_31 @cite_20 @cite_13 @cite_12 @cite_17
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Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learning-by-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, appearance variability, and the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses real-time approximations for complex eyeball materials and structures as well as anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework available online for the benefit of the research community. Existing appearance-based gaze estimation methods suffer from tedious calibration and appearance variation caused by head movement. In this paper, to handle this problem, we propose a novel appearance-based gaze estimation method by introducing supervised adaptive feature extraction and hierarchical mapping model. Firstly, an adaptive feature learning method is proposed to extract topology-preserving (TOP) feature individually. Then hierarchical mapping method is proposed to localize gaze position based on coarse-to-fine strategy. Appearance synthesis approach is used to increase the refer sample density. Experiments show that under the condition of sparse calibration, proposed method has better performance in accuracy than existing methods under fixed head pose without chinrest. Moreover, our method can be easily extended for head pose-varying gaze estimation. A large amount of training data is usually crucial for successful supervised learning. However, the task of providing training samples is often time-consuming, involving a considerable amount of tedious manual work. In addition, the amount of training data available is often limited. As an alternative, in this paper, we discuss how best to augment the available data for the application of automatic facial landmark detection. We propose the use of a 3D morphable face model to generate synthesized faces for a regression-based detector training. Benefiting from the large synthetic training data, the learned detector is shown to exhibit a better capability to detect the landmarks of a face with pose variations. Furthermore, the synthesized training data set provides accurate and consistent landmarks automatically as compared to the landmarks annotated manually, especially for occluded facial parts. The synthetic data and real data are from different domains; hence the detector trained using only synthesized faces does not generalize well to real faces. To deal with this problem, we propose a cascaded collaborative regression algorithm, which generates a cascaded shape updater that has the ability to overcome the difficulties caused by pose variations, as well as achieving better accuracy when applied to real faces. The training is based on a mix of synthetic and real image data with the mixing controlled by a dynamic mixture weighting schedule. Initially, the training uses heavily the synthetic data, as this can model the gross variations between the various poses. As the training proceeds, progressively more of the natural images are incorporated, as these can model finer detail. To improve the performance of the proposed algorithm further, we designed a dynamic multi-scale local feature extraction method, which captures more informative local features for detector training. An extensive evaluation on both controlled and uncontrolled face data sets demonstrates the merit of the proposed algorithm. In this paper, we address the problem of free head motion in appearance-based gaze estimation. This problem remains challenging because head motion changes eye appearance significantly, and thus, training images captured for an original head pose cannot handle test images captured for other head poses. To overcome this difficulty, we propose a novel gaze estimation method that handles free head motion via eye image synthesis based on a single camera. Compared with conventional fixed head pose methods with original training images, our method only captures four additional eye images under four reference head poses, and then, precisely synthesizes new training images for other unseen head poses in estimation. To this end, we propose a single-directional (SD) flow model to efficiently handle eye image variations due to head motion. We show how to estimate SD flows for reference head poses first, and then use them to produce new SD flows for training image synthesis. Finally, with synthetic training images, joint optimization is applied that simultaneously solves an eye image alignment and a gaze estimation. Evaluation of the method was conducted through experiments to assess its performance and demonstrate its effectiveness.