With the surge in remote work, we are dependent on high quality video conferencing solutions. However, low-bandwidth connections, poor image quality and lighting, and lack of engagement tools significantly degrade the experience. Leading companies are leveraging our synthetic facial data to train new machine learning models to improve the video quality and teleconferencing experience.
Portrait Segmentation & Matting
Background blurring or replacement is currently the most used feature in video conferencing. As we have all experienced, the segmentation performance of today’s systems is not robust across lighting and environments. To develop better models, a broader set of scenarios is needed — and for this reasons, we provide a variety of datasets to address the issues our faces’… face.
Eye Gaze Estimation
See things through their eyes by implementing gaze detection. Understanding gaze lets your application gauge attentiveness levels as well as deduce spatial relationships between important objects in meetings. Gaze correction to adjust for camera offsets also helps meetings feel more natural and connected.
Image
Optimization
Not being able to see because of atrocious lighting can kill the productivity of any meeting. Lag can stop it in its tracks. With upscaling and re-lighting techniques, attendees will never even know there was a bandwidth hiccup or challenging lighting to begin with.
Ready for a demo or want to learn more? Get in touch.
