Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.

Abstract: A system for adjusting an interpupillary distance and an eye relief distance of a headset. The system can determine an interpupillary distance of a user and an eye relief distance of the user. The system can cause an adjustment assembly to adjust a first distance between the first lens and the second lens along a first axis based at least in part on the interpupillary distance and cause the adjustment assembly to adjust the optical assembly a second distance along a second axis that is orthogonal to the first axis based at least in part on the eye relief distance.

Abstract: The present disclosure is directed to processing imagery using one or more machine learning (ML) models. In particular, data describing imagery comprising a plurality of different and distinct frames can be received; and based at least in part on one or more ML models and the data describing the imagery, and for each frame of the plurality of different and distinct frames, one or more scores can be determined for the frame. Each score of the score(s) can indicate a determined measure of suitability of the frame with respect to one or more of various different and distinct uses for which the ML model(s) are configured to determine suitability of imagery.