Deep Pictorial Gaze Estimation

Park, Seonwook; Spurr, Adrian; Hilliges, Otmar

doi:10.1007/978-3-030-01261-8_44

Cited by 163 publications

(117 citation statements)

References 44 publications

Supporting

Mentioning

105

Contrasting

Order By: Relevance

“…Gaze Estimation Error. For the assessment of gaze redirection accuracy, we employ a state-of-the-art gaze estimator proposed by Park et al [24] which was pre-trained on MPIIGaze [34]. The estimator predicts the gaze direction of the generated gaze images.…”

Section: Metricsmentioning

confidence: 99%

Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

Spurr

Zhang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

View full text Add to dashboard Cite

Gaze redirection is the task of changing the gaze to a desired direction for a given monocular eye patch image. Many applications such as videoconferencing, films, games, and generation of training data for gaze estimation require redirecting the gaze, without distorting the appearance of the area surrounding the eye and while producing photorealistic images. Existing methods lack the ability to generate perceptually plausible images. In this work, we present a novel method to alleviate this problem by leveraging generative adversarial training to synthesize an eye image conditioned on a target gaze direction. Our method ensures perceptual similarity and consistency of synthesized images to the real images. Furthermore, a gaze estimation loss is used to control the gaze direction accurately. To attain highquality images, we incorporate perceptual and cycle consistency losses into our architecture. In extensive evaluations we show that the proposed method outperforms state-of-theart approaches in terms of both image quality and redirection precision. Finally, we show that generated images can bring significant improvement for the gaze estimation task if used to augment real training data.

show abstract

Section: Metricsmentioning

confidence: 99%

Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

Spurr

Zhang

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Note that all the aforementioned methods are used for semantic segmentation. Recently, however, the FCN-like network structure has been also applied successfully to other keypoint detection problems such as human pose estimation [58], facial landmark detection [59] and eye gaze estimation [60,61]. They all have an encoder-decoder architecture and used a FCN-like network structure called hourglass network which borrows the idea from FCN.…”

Section: Related Workmentioning

confidence: 99%

Accurate and robust eye center localization via fully convolutional networks

Xia

Wang

2019

IEEE/CAA J. Autom. Sinica

View full text Add to dashboard Cite

Eye center localization is one of the most crucial and basic requirements for some human-computer interaction applications such as eye gaze estimation and eye tracking. There is a large body of works on this topic in recent years, but the accuracy still needs to be improved due to challenges in appearance such as the high variability of shapes, lighting conditions, viewing angles and possible occlusions. To address these problems and limitations, we propose a novel approach in this paper for the eye center localization with a fully convolutional network (FCN), which is an endto-end and pixels-to-pixels network and can locate the eye center accurately. The key idea is to apply the FCN from the object semantic segmentation task to the eye center localization task since the problem of eye center localization can be regarded as a special semantic segmentation problem. We adapt contemporary FCN into a shallow structure with a large kernel convolutional block and transfer their performance from semantic segmentation to the eye center localization task by fine-tuning. Extensive experiments show that the proposed method outperforms the state-of-the-art methods in both accuracy and reliability of eye center localization. The proposed method has achieved a large performance improvement on the most challenging database and it thus provides a promising solution to some challenging applications.

show abstract

“…Rows from top to bottom show input test images, ground truth labels, predictions from mSegNet w/BR [4] and predictions from RITnet, respectively. semantic segmentation of these regions enables the extrac- tion of region-specific features (e.g., iridial feature tracking [2])and mathematical models which summarize the region structures (e.g., iris ellipse [17,1,13], or pupil ellipse [7]) used to derive a measure of gaze orientation.…”

Section: Introductionmentioning

confidence: 99%

RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking

Chaudhary

Kothari

Acharya

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

View full text Add to dashboard Cite

Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time. Here, we present the RITnet model, which is a deep neural network that combines U-Net and DenseNet. RITnet is under 1 MB and achieves 95.3% accuracy on the 2019 OpenEDS Semantic Segmentation challenge. Using a GeForce GTX 1080 Ti, RITnet tracks at > 300Hz, enabling real-time gaze tracking applications. Pre-trained models and source code are available 1 . * Equal Contribution. 1 https://bitbucket.org/eye-ush/ritnet/

show abstract

Deep Pictorial Gaze Estimation

Cited by 163 publications

References 44 publications

Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks

Accurate and robust eye center localization via fully convolutional networks

RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking

Contact Info

Product

Resources

About