Abstract: Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.

Abstract: Techniques are described for providing a multi-cloud control plane (MCCP) in a first cloud infrastructure (included in a first cloud environment provided by a first cloud services provider) that enables services and/or resources provided in the first cloud infrastructure to be utilized by users of a second cloud environment, where the second cloud environment is different than the first cloud environment. The multi-cloud infrastructure enables a user associated with an account with a second cloud services provider to use, from the second cloud infrastructure, a first service from the set of one or more cloud services. The multi-cloud infrastructure creates a link between the account with the second cloud service provider and a tenancy created in the first cloud infrastructure for enabling using the first service by the user.

Abstract: A gesture-based wake process for an AR system is described herein. The AR system places a hand-tracking input pipeline of the AR system in a suspended mode. A camera component of the hand-tracking input pipeline detects a possible visual wake command being made by a user of the AR system. On the basis of detecting the possible visual wake command, the AR system wakes the hand-tracking input pipeline and places the camera component in a fully operational mode. If the AR system, using the hand-tracking input pipeline, verifies the possible visual wake command as an actual wake command, the AR system initiates execution of an AR application.

Abstract: Methods and systems are disclosed for performing operations comprising: receiving a monocular image that includes a depiction of a whole body of a user; generating a segmentation of the whole body of the user based on the monocular image; accessing a video feed comprising a plurality of monocular images received prior to the monocular image; smoothing, using the video feed, the segmentation of the whole body generated based on the monocular image to provide a smoothed segmentation; and applying one or more visual effects to the monocular image based on the smoothed segmentation.

Abstract: A gesture-based wake process for an AR system is described herein. The AR system places a hand-tracking input pipeline of the AR system in a suspended mode. A camera component of the hand-tracking input pipeline detects a possible visual wake command being made by a user of the AR system. On the basis of detecting the possible visual wake command, the AR system wakes the hand-tracking input pipeline and places the camera component in a fully operational mode. If the AR system, using the hand-tracking input pipeline, verifies the possible visual wake command as an actual wake command, the AR system initiates execution of an AR application.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Abstract: Methods and systems are disclosed for performing operations comprising: receiving a monocular image that includes a depiction of a whole body of a user; generating a segmentation of the whole body of the user based on the monocular image; accessing a video feed comprising a plurality of monocular images received prior to the monocular image; smoothing, using the video feed, the segmentation of the whole body generated based on the monocular image to provide a smoothed segmentation; and applying one or more visual effects to the monocular image based on the smoothed segmentation.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Abstract: Techniques for training a neural network having a plurality of computational layers with associated weights and activations for computational layers in fixed-point formats include determining an optimal fractional length for weights and activations for the computational layers; training a learned clipping-level with fixed-point quantization using a PACT process for the computational layers; and quantizing on effective weights that fuses a weight of a convolution layer with a weight and running variance from a batch normalization layer. A fractional length for weights of the computational layers is determined from current values of weights using the determined optimal fractional length for the weights of the computational layers. A fixed-point activation between adjacent computational layers is related using PACT quantization of the clipping-level and an activation fractional length from a node in a following computational layer.

Abstract: Methods and systems are disclosed for performing operations comprising: receiving a monocular image that includes a depiction of a whole body of a user; generating a segmentation of the whole body of the user based on the monocular image; accessing a video feed comprising a plurality of monocular images received prior to the monocular image; smoothing, using the video feed, the segmentation of the whole body generated based on the monocular image to provide a smoothed segmentation; and applying one or more visual effects to the monocular image based on the smoothed segmentation.

Abstract: Methods and systems are disclosed for performing operations comprising: receiving a monocular image that includes a depiction of a whole body of a user; generating a segmentation of the whole body of the user based on the monocular image by applying one or more machine learning techniques; receiving input that selects a visualization mode; and applying one or more visual effects corresponding to the visualization mode to the monocular image based on the segmentation.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.

Abstract: A compact generative neural network can be distilled from a teacher generative neural network using a training network. The compact network can be trained on the input data and output data of the teacher network. The training network train the student network using a discrimination layer and one or more types of losses, such as perception loss and adversarial loss.