Abstract: Systems, apparatuses and methods may provide for technology that encodes multi-view visual data into latent features via an aggregator encoder, decodes the latent features into one or more novel target views different from views of the multi-view visual data via a rendering decoder, and decodes the latent features into an object label via a label decoder. The operation to decode the latent features via the rendering decoder and to decode the latent features via the label decoder occur at least partially at the same time. The operation to encode, via the aggregator encoder, the multi-view visual data into the latent features further includes operations to: perform, via the aggregator encoder, semantic object recognition operations based on radiance field view synthesis operations, and perform, via the aggregator encoder, radiance field view synthesis operations based on semantic object recognition operations.

Abstract: An apparatus to facilitate inferred object shading is disclosed. The apparatus comprises one or more processors to receive rasterized pixel data and hierarchical data associated with one or more objects and perform an inferred shading operation on the rasterized pixel data, including using one or more trained neural networks to perform texture and lighting on the rasterized pixel data to generate a pixel output, wherein the one or more trained neural networks uses the hierarchical data to learn a three-dimensional (3D) geometry, latent space and representation of the one or more objects.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed for super-resolution rendering. An example apparatus includes a data handler to generate a multi-sample control surface (MCS) frame based on a color frame, a feature extractor to obtain features from the color frame, a depth frame, and the MCS frame, a network controller to generate spatial data and temporal data based on the features, and a reconstructor to generate a high-resolution image based on the features, the spatial data, and the temporal data.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data, wherein the sensor data is captured by a plurality of sensors within an educational environment. The processor is to: access the sensor data captured by the plurality of sensors; identify a student within the educational environment based on the sensor data; detect a plurality of events associated with the student based on the sensor data, wherein each event is indicative of an attention level of the student within the educational environment; generate a report based on the plurality of events associated with the student; and send the report to a third party associated with the student.