27 May 2018

Oculus Hand-Tracking Accuracy

One of Facebook’s underlying goals for VR is to use it as a means of connecting distant people. While today friends can talk and play in a range of social VR applications, including Facebook Spaces, the representation of users in VR is still a caricature at best. Recently, Oculus showed work being done on hand-tracking to bring more intuitive control and accurate avatars into VR. Oculus ‘Half Dome’ prototype is a headset with a 140 degree field of view and varifocal display. A computer-vision based hand-tracking system trained with a self-optimizing machine learning algorithm, achieves tracking that’s far more accurate than any method before for tracking a single hand, two hands, and hand-object interactions. Footage which appeared to show the hand-tracking in action also appeared to show detection of snapping gestures.

The company used a marker-based tracking system to record hand interactions in high fidelity, and then condensed the recorded data into 2D imagery which allowed them to set a convolutional neural network to the task of uniquely identifying the positions of the markers across a large set of hand pose imagery, effectively allowing the system to learn what a hand should look like given an arbitrary set of marker positions. Ostensibly, this trained system can then be fed markerless camera input of a user’s hands and solve for their position. By measure of something Oculus labeled ‘Tracking Success Rate’ (the company claims to have achieved a rather astounding 100% success rate with single hand-tracking). They claim even bigger leaps compared to other methods for two-handed and hand-object interactions.

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