Github Figlab Rgbdgaze This is the research repository for rgbdgaze: gaze tracking on smartphones with rgb and depth data, presented at acm icmi 2022. it contains the training code and dataset link. Our mobile rgbd dataset of 50 participants (which we make freely available at github figlab rgbdgaze) is the first of its kind, offering rgbd data paired with user gaze location across a variety of use contexts.
Github Figlab Rgbdgaze Our mobile rgbd dataset of 50 participants is the first of its kind, offering rgbd data paired with user gaze location across a variety of use contexts. we implemented a cnn model based on a spatial weights structure to efficiently fuse the rgb and depth modalities. Our system and dataset offer the first benchmark of gaze tracking on smartphones using rgb depth data under different use contexts. In this paper, we present a gaze tracking system that makes use of today’s smartphone depth camera technology to adapt to the changes in distance and orientation relative to the user’s face. unlike prior efforts that used depth sensors, we do not constrain the users to maintain a fixed head position. Bibliographic details on rgbdgaze: gaze tracking on smartphones with rgb and depth data.
Github Figlab Super Resolution Dataset In this paper, we present a gaze tracking system that makes use of today’s smartphone depth camera technology to adapt to the changes in distance and orientation relative to the user’s face. unlike prior efforts that used depth sensors, we do not constrain the users to maintain a fixed head position. Bibliographic details on rgbdgaze: gaze tracking on smartphones with rgb and depth data. We leverage machine learning to demonstrate accurate smartphone based eye tracking without any additional hardware. we show that the accuracy of our method is comparable to state of the art mobile. Rgbdgaze: gaze tracking on smartphones with rgb and depth data computer interfaces with the ability to track a user’s on screen gaze location offer the potential for more accessible and powerful multimodal interactions, perhaps one day even supplanting the venerable cursor. In this paper, we present a gaze tracking system that makes use of today’s smartphone depth camera technology to adapt to the changes in distance and orientation relative to the user’s face. unlike prior eforts that used depth sensors, we do not constrain the users to maintain a fxed head position. By using a microphone array integrated into a user's headset glasses, we can use beamforming to create a virtual microphone that tracks with the user's fingers in 3d space.
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