114 citations · 145 across the 3 of their papers we have counts for
8 papers
Self-Learning Transformations for Improving Gaze and Head Redirection
Yufeng Zheng, Seonwook Park, Xucong Zhang +2
Many computer vision tasks rely on labeled data. Rapid progress in generative modeling has led to the ability to synthesize photorealistic images. However, controlling specific asp…
ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head Pose and Gaze Variation
Xucong Zhang, Seonwook Park, Thabo Beeler +3
Gaze estimation is a fundamental task in many applications of computer vision, human computer interaction and robotics. Many state-of-the-art methods are trained and tested on cust…
Towards End-to-end Video-based Eye-Tracking
Seonwook Park, Emre Aksan, Xucong Zhang +1
Estimating eye-gaze from images alone is a challenging task, in large parts due to un-observable person-specific factors. Achieving high accuracy typically requires labeled data fr…
Content-Consistent Generation of Realistic Eyes with Style
Marcel Bühler, Seonwook Park, Shalini De Mello +2
Accurately labeled real-world training data can be scarce, and hence recent works adapt, modify or generate images to boost target datasets. However, retaining relevant details fro…
Photo-Realistic Monocular Gaze Redirection Using Generative Adversarial Networks
Zhe He, Adrian Spurr, Xucong Zhang +1
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 gener…
Evaluation of Appearance-Based Methods and Implications for Gaze-Based Applications
Xucong Zhang, Yusuke Sugano, Andreas Bulling
Appearance-based gaze estimation methods that only require an off-the-shelf camera have significantly improved but they are still not yet widely used in the human-computer interact…