Abstract: In various embodiments, an encoding metric comparison application computes a first set of quality scores associated with a test encoding configuration based on a set of bootstrap quality models. Each bootstrap quality model is trained based on a different subset of a training database. The encoding metric comparison application computes a second set of quality scores associated with a reference encoding configuration based on the set of bootstrap quality models. Subsequently, the encoding metric comparison application generates a distribution of bootstrap values for an encoding comparison metric based on the first set of quality scores and the second set of quality scores. The distribution quantifies an accuracy of a baseline value for the encoding comparison metric generated by a baseline quality model.

Abstract: In various embodiments, a bootstrapping training subsystem performs sampling operation(s) on a training database that includes subjective scores to generate resampled dataset. For each resampled dataset, the bootstrapping training subsystem performs machine learning operation(s) to generate a different bootstrap perceptual quality model. The bootstrapping training subsystem then uses the bootstrap perceptual quality models to quantify the accuracy of a perceptual quality score generated by a baseline perceptual quality model for a portion of encoded video content. Advantageously, relative to prior art solutions in which the accuracy of a perceptual quality score is unknown, the bootstrap perceptual quality models enable developers and software applications to draw more valid conclusions and/or more reliably optimize encoding operations based on the perceptual quality score.

Abstract: In various embodiments, an encoding metric comparison application computes a first set of quality scores associated with a test encoding configuration based on a set of bootstrap quality models. Each bootstrap quality model is trained based on a different subset of a training database. The encoding metric comparison application computes a second set of quality scores associated with a reference encoding configuration based on the set of bootstrap quality models. Subsequently, the encoding metric comparison application generates a distribution of bootstrap values for an encoding comparison metric based on the first set of quality scores and the second set of quality scores. The distribution quantifies an accuracy of a baseline value for the encoding comparison metric generated by a baseline quality model.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.

Abstract: One embodiment of the present invention sets forth a technique for generating a three-dimensional (3D) model of an individual. The technique involves receiving pose data that includes image data associated with the individual being in a pose, selecting from a database a morphable 3D generic human model based on a gender of the individual, adjusting a pose of the morphable 3D generic model based on the image data, identifying, based on the image data, one or more 3D appearance parameter objects included in the database, and adjusting via one or more processors one or more properties of the morphable 3D generic model based on the image data as well as the one or more 3D appearance parameter objects to produce the 3D model of the individual.